business needs section 10
TRANSCRIPT
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 112
Solution Alignment with BMS’
Business Needs
Section 10.0 RFP reference: 4.1.10, Solution Alignment with BMS’ Business Needs, Page 51
Vendor should describe in detail how the solution proposed provides the functionality identified in this RFP as necessary to meet BMS’ current business needs and the work of the work units listed. Vendor should also describe how the proposed solution provides the foundation that enables BMS to move toward its vision for its future MITA-oriented Medicaid Enterprise. Vendor should demonstrate in its proposal how the solution provides BMS the ability to perform more sophisticated analyses to make better decisions, improve health outcomes, and make best use of state and federal financial resources through financial analysis; defined and ad hoc reporting capabilities; clinical utilization and care management case analysis; and analytics such as trending and what-if scenarios. The vendor can include additional materials, in a separately labeled section at the back of the proposal, which describes company offerings that should be of value to BMS, but this section would not be reviewed as a formal section of the RFP. The vendor should complete the checklist columns of Appendix 2 – Detailed Business and Technical Requirements, Section A.
Deloitte’s goal is to exceed the Bureau for Medical Services’ (BMS’)
expectations for the implementation of a West Virginia (WV) Medicaid
Data Warehouse/Decision Support System (DW/DSS). Our Design,
Development, and Implementation (DDI) and Operate and Enhance
Phase approach will emphasize the processes involved in classic
project management methodologies. By focusing on the processes
involved, we will confirm that a systematic approach is defined and
followed. This approach includes adhering to a very detailed project
plan, establishing the organizational structure, and implementing our
documented processes that are tried and true from past experience.
Deloitte is committed to the delivery of exceptional service for the BMS’
DW/DSS design, development, implementation, operation and
enhancements.
The DW/DSS project represents a significant initiative for BMS as it
supports the strategic vision for the West Virginia Medicaid program
and it also attains the goals identified during the Medicaid Information
Technology Architecture (MITA) State Self-Assessment (SS-A). Our
approach will assist BMS with its migration towards a MITA-oriented enterprise.
Deloitte is committed to MITA because it believes that the age of legacy IT systems and unitary data
warehousing has passed. SOA architecture supports the seamless integration of vendor packages to support
many aspects of performance monitoring and outcomes management, and other powerful data analytical
tools. Deloitte will make use of these tools and believes that the flexibility afforded by MITA is an important
step in the right direction. Our solution approach will address how we plan to support BMS’ Business Areas.
Team Deloitte
Differentiators
Prior relevant, successful
experience with public sector
data warehousing projects
40-year track record
successfully delivering Health
Sciences and Government
consulting projects
Ranked the “#1 Health Care
and Life Sciences
consultancy” by Kennedy
Information Services
Gartner identified Deloitte to
be a leader provider of
business intelligence and
performance management
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 113
Figure 10-1.
While the DW/DSS is intended to support the information and analytical needs of several BMS work units,
i.e., MITA business areas, our solution is flexible enough to support additional business areas as well as
other agencies and business partners outside of BMS and DHHR moving forward. Our understanding of
BMS’ business requirements, coupled with our approach to solution design and delivery, supports both the
immediate and long-term requirements identified by BMS.
Deloitte’s solution approach will align with the MITA Business Process Areas, enabling BMS to support the
MITA Business Model. BMS has initially identified goals in four MITA business areas that can be either fully
or partially met through the DW/DSS implementation.
Operations Management
− Reduce the potential for redundancy in services and payments.
− Improve access to information.
− Enhance and automate reporting capabilities to measure compliance with operational performance
measures.
− Improve operational efficiency and reduce costs in the healthcare system.
Program Management
− Enhance decision and policy-making capabilities through data analysis.
− Enhance the ability to analyze the effectiveness of potential and existing benefits and policies through
the integration of claims data with clinical data.
WV_DW_DSS-063
BMS Work Units MITA Business Areas
Finance Program/Operations Management
Program Management
Operations Management
Program Integrity Management
Program Management
Program Integrity Management
Program Management
Pharmacy
MMIS Operations
Office of Quality and Program Integrity (OQPI)
Program Policy
Medicaid Fraud Control Unit (MFCU)
Technology and Reporting
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 114
Care Management
− Improve healthcare outcomes for members.
− Establish access to data from other programs, agencies or entities.
Program Integrity Management
− Use decision support capability to support SUR activities.
− Improve data access, data accuracy, and the accuracy of process results, while reducing the effort
required to achieve these results.
DW/DSS Solution
Deloitte has extensive experience working with state Medicaid agencies that have taken the initiative to
implement a data warehouse and decision support systems. Based on our experience, we can easily predict
the issues and recognize the frustrations that an agency will likely encounter as it attempts to implement the
current tools and systems. We understand both the operational efforts that business areas go through to
operationalize the new system tools and the difficulties end users have in using these tools to both gain
insight and identify actionable information.
Over time these current vendors have either pieced together siloed tools, or developed custom
subcomponents tacked on to standard COTS tools, in order to deploy a system that addresses an agency’s
base Medicaid decision support system requirement. These approaches make the system deployment much
more complicated, which in turn creates both greater risk and more potential areas for failure.
Deloitte’s approach is to have a single complete and deployable solution, not merely a system but rather a
true solution, that will leverage our years of experience in enabling operations with technology systems and
tools. Our solution will build upon our experience gained over the years when we assisted Medicaid
agencies’ deployment of data warehouses and decision support systems. We know the issues, limitations,
and complications of other tools in the market as we have developed business processes and trained
resources to incorporate these as best as possible into day-to-day operations. In developing our proposed
solution, we have leveraged the full spectrum of Deloitte’s knowledge and experience.
Through our experience, Deloitte has designed an innovative, leading edge Data Warehouse and Decision
Support Solution, one that not only focuses on business intelligence but rather one that combines business
and clinical intelligence (BCI). We believe that Medicaid agencies need to change how they analyze and use
the wealth of historical data to enable their operations. They need to transform from the current standard
retrospective cost-based management approach to one that is prospective and addresses the cause of the
high costs, specifically the health status and the quality of care delivery, focusing on population
management.
Our approach will feed the analytical requirements across a Medicaid agency’s business areas and will open
the lines of communication, as every operational and analytic report, trend analyses, case identification, etc.
will draw upon a common foundation of data, a central repository of historical and current actionable
information. This data warehouse will aggregate all data into a single warehouse to include, but not be
limited to, member eligibility, medical claims, encounters, pharmacy, lab values, provider details, risk
assessments, and care management programs. The data warehouse will be the only central repository and
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 115
will be developed using open architectural standards and not a proprietary closed system. These open
standards will support the increases in data volume, the expansion of data elements, and these will also
allow easy access using standard commercial-off-the-shelf tools, all supporting the MITA architecture.
Figure 10-2. Proposed Solution Diagram.
Deloitte’s proposed solution diagram above illustrates how our solution addresses each core business area.
Below we will show areas in the Solution Diagram that directly address the business requirements identified
in the RFP and how they are broken out by business area. The Solution Map thus enables us to provide a
more detailed discussion of plans for addressing each business requirement and to document which parts of
the solution will effectuate those plans. The Solution Map contains five core areas in the application:
1. Data Extraction. Data Extraction is the area of the DW/DSS solution which contains the extract
requirements and ETL mappings to load the data from either flat files or through direct interface into the
Lab Vendor
MCOs
MMIS
WV_DW_DSS-001_4
BMS DSS Web-Portal
RAPIDS
Medical Claims
Pharmacy
Eligibility
Encounters
Provider
Lab Results
Medical
ClaimsEncounters
Member
Eligibility
Lab
ResultsPharmacy
Processed
Analytics
Provider
Care
Management
Data
Processing
Business
Analytics
Clinical Grouper
(CRGs)
Clinical
Analytics
Data
Cleansing
&
Validation
Quality
Assurance
and Data
Integrity
Report
Consolidated Cleansed Data
Raw Data from Extracts
Data
Warehouse
Operations Management
Utilization Financial
Care Management
Materialized
Views and
Data Cubes
Population Utilization
Member Quality
Outcomes
Program Management
Program Integrity
Management
Utilization (Services
Rendered)
Financial (Retrospective)
Outcomes Financial (Prospective)
Pharmacy Rate Setting
Provider Vendor
Fraud &
Abuse
Surveillance
Utilization
Personal Health Record
(PHR)
Ad-hoc Reports
Eligibility
Reference
External Data
Dashboard
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 116
source data systems. This area will focus on the loading of the raw data elements from a landing area into
load tables. Prior to populating the load tables, initial validation is performed to confirm that the data received
meets the basic conditional requirements, i.e., field size, element constraints, character types, element
delimitation, etc.
2. Data Cleansing. Data Cleansing is the area that will take the data post extraction, from the load tables,
and will process it through a series of data cleansing and detailed validation mappings. This process will
assess the data elements for validity, completeness, and reasonableness. Claim adjustments are carefully
netted against the original claims to produce accurate ―net payment‖ reports. This process is complete once
a validation process verifies the output files are consistent with inputs. Errors or issues with the data from this
process will be reported.
3. Data Storage. Data Storage is the area of the DW/DSS which physically contains the database, data
tables, reference tables, views, dimensions, and other raw data that make up the DW. This data will be
acquired from the different sources. This area will directly support the data processing and analytical areas of
the DW/DSS solution. The data storage will contain finalized MMIS claims data that will be reconciled to
payment and clinical data as well as eligibility data, provider data, MCO encounter data, reference data, and
lab results data.
4. Data Processing. Data Processing is the area of our solution that begins applying the business and
clinical intelligence. The cleansed data stored in the DW will be drawn upon and run through a series of
processes that will break down the data into actionable segments where we can act upon the details to be
processed through our clinical grouper, run through our business and clinical analytics, and formulated to
details and then loaded into the appropriate DW tables. This process creates the data elements that form the
basis of our information delivery.
5. Information Delivery. Information Delivery is the BMS DSS Information Portal (i-Portal). This information
portal is the mechanism with which users interact to both present and disseminate information. The i-Portal is
a single application, COGNOS, along with the SPSS analytical and predictive modeling module, which gives
business users a one stop analytical tool to analyze the Medicaid data. This portal will have the access
security that will assign authorization entitlements to users, only allowing them to see the areas they are
authorized to see, and it will also track user navigation. i-Portal will be broken out by business area and will
provide users access to defined reports and ad hoc analyses.
Deloitte’s Solution will provide both significant operational improvements and demonstrable savings. By
strengthening the architecture and data access components of the DW/DSS, BMS will realize improvements
in five (5) critical operations areas:
1. Scalability. The DW/DSS can grow in size to accommodate future program growth and potentially
voluminous new files from external data sources as envisioned in the RFP.
2. Extensibility. New subject areas can be added as Medicaid Reform and other program initiatives require
new data sets and analyses.
3. Flexibility. The DW/DSS can be quickly modified to meet changing business needs and can support a
variety of needs from simple queries to complex statistical analyses and major multi-user program planning
exercises.
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 117
4. Interoperability. The DW/DSS can freely receive and exchange data with other systems and assure that
BMS can support expansion and will be a key player in the evolution of a National Health Information System
(NHIS).
5. Usability. All users will be able to access the DW/DSS, query its’ data, and receive information back
quickly without reliance on specialized technical assistance.
Solution Approach
Deloitte’s transformed BCI-DSS solution is an innovative, integrated secure Web-based decision support
system solution that directly draws upon the data stored in a data warehouse. It aligns both business and
clinical intelligence, and integrates data informatics, operational support, and outcomes management into
day-to-day operations. With these combined capabilities, BCI-DSS enables the operational spectrum by
drawing upon a common foundation of data to be proactive and to share decision support information
between Medicaid business areas, as well as their MCOs and providers.
Deloitte’s BCI-DSS solution will improve BMS’ ability to manage its population by providing a detailed
understanding of its health status and the progression of diseases, the quality of care and delivery of
services, resource consumption and service utilization. This understanding will identify impact areas to
develop case and operational interventions while managing them to outcomes.
BCI-DSS processes the raw cleansed data into a clinically-based stratification model that provides a solid
foundation to identify both outliers and impact areas based on the clinical component of an individual’s health
status. BCI-DSS’s analyses will easily identify outliers due to the focus on the quality aspects of care and
care delivery at the member and provider service levels, introducing the cost component prospectively. BCI-
DSS utilizes the quality indicators and disease burden as the primary drivers of outlier identification,
providing the methodology not only to identify outliers but also the means to track interventional outcomes
and cost savings.
Our solution enables program planning, policy analysis, evaluation, and performance monitoring. This is
accomplished with the use of cohort studies that monitor the impact of various programs and policies on the
changes in the disease burden and other parameters. Quality of care and outcomes assessment are easily
measured through the various quality parameters captured, including ambulatory sensitive admissions,
disease complications, gaps in care (including immunizations, various disease centered services like foot
care, eye care for diabetics) and other parameters developed from AHRQ and HEDIS recommendations.
Most HEDIS measures are part of the quality component of BCI-DSS and these can be modified by request
to accommodate any program specific performance measures.
BCI-DSS combines industry experience and advanced analytics to create an integrated delivery solution that
can support a wide range of business area operations to include:
Operations Management Population Analysis
Utilization Analysis
Financial Analysis
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 118
Program Management Utilization Analysis
Financial Analysis
Pharmacy Analysis
Provider Analysis
Vendor Management
MCO Management
Outcomes Tracking
Rate Setting
Care Management Population Analysis
Member Analysis
Utilization Analysis
Quality Analysis
Outcomes Tracking
Program Integrity Management Surveillance and Utilization
Quality Outliers (Fraud & Abuse)
Deloitte understands the phased implementation that BMS has defined in the RFP. We believe that the
approach BMS has outlined is necessary with such a large initiative. Throughout our experience we have
seen many aggressive implementation timelines fail. The phased implementation and enhancement of a
DW/DSS will assist BMS to achieve its goals and to move toward its MITA oriented Medicaid enterprise.
Our solution is designed to support growth and future enhancements of the DW/DSS to easily include the
ability to add data from additional State agencies and potentially enable data access for additional State and
external entities. Our solution is streamlined, will be implemented on all open standards, will have no
proprietary components, and will be developed using a single user application interface, COGNOS along
with their SPSS Module.
Deloitte’s solution will improve information delivery and data access through the use of the proposed BCI-
DSS i-Portal approach. i-Portal and the underlying analytics will enhance BMS’ reporting capabilities and will
provide intelligent information to the end-users to make day-to-day decisions. Our solution is designed for all
levels of resources and is not designed for only the technical resources to run queries. Our goal in designing
our solution is to deliver information to all of the users.
Through the detailed analyses described below, BMS will have the ability to link financials to outcomes and
easily establish pay-for-performance (P4P) initiatives as well as monitor performances of various programs
against contracted service level agreements (SLAs). Performance monitoring will be enhanced through the
integration of detailed clinical data, i.e., lab results and pharmacy data.
We are focused on delivering a true solution rather than just another product or system. We will provide the
right resources to train and provide the experienced guidance on how to integrate i-Portal and the BCI-DSS
analytics into the business and operational areas. We will provide the clinical and analytic experienced
resources to help interpret the information and train your resources to set interventions and to manage them
to outcomes.
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 119
Underlying Clinical Data Stratification
Deloitte’s BCI-DSS solution provides a layer of analytical capabilities
that other vendors cannot provide. We not only provide a world class
data architecture to streamline the analytical process, but we also
provide a detailed clinical stratification that will truly provide the means
to combine the business and clinical aspects of the captured data. Our
design centers on the members’ data from which everything else is
derived, i.e., provider performance is dependent on how care is
delivered to their patients, financial impacts are dependent on the
resource consumption of services to members, etc.
Our solution leverages over 26 years of clinical, financial and
administrative expertise of 3M™ Health Information Systems (HIS)
and their world class classification and grouping solutions, Clinical
Risk Grouping software (CRGs). The CRGs grouping software
provides the underlying clinical categorization of the individual
members by disease or combination of diseases (co-morbidities) as
well as the state of their progression (i.e., the severity of the illness).
3M’s Clinical Risk Grouping Software
We chose to use 3M’s CRGs over other market grouping software
because of 3M’s advanced design that follows the clinical based
categorical model with progression determination (i.e., severity
adjustment), a feature that is not found in any other grouping software.
Other grouping software in the market supports the cost based
statistical model that has been used in the industry for twenty plus
years.
Using the CRGs as the clinical stratification model, all individuals with
the same disease(s) are not categorized in the same bucket, as is the
case with other groupers. With CRGs, for example, you can easily
identify all individuals that have a single disease of Diabetes (CRGs
status 5), but the group of individuals will be adjusted based on the
progression of their individual disease and would be distributed across
4 different severity levels. This status and severity adjustment is the
individual’s health status. This will allow BMS to track and trend the
progression of an individual’s health status and over time will track
how the severity level increases as individuals move into a higher
status.
Unique features of 3MTM
Clinical Risk Grouping
Software:
• Statistical performance
superior to any other
available risk adjustment
system
• Complete specifications
of clinical logic provided
with software- no black
box
• Explicit severity of
illness levels for all
chronic illnesses
• Ability to obtain detailed
breakdowns of the types
and amounts of services
provided for clinically
comparable individuals
• Based on standard claims
data
• Can be used with varying
degrees of data
completeness
• Can be used both
prospectively and
retrospectively
• Give you the ability to
analyze all or part of your
resource expenditures
• Give you the ability to
compare results to
external populations
Unique andDistinguishingFactors
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 120
Figure 10-3. Individual Health Status Progression.
The above graph demonstrates the ability to trend the health status progression of an individual, which can
also be accomplished for various populations, which is discussed below under Population Management. The
graph shows an individual with Diabetes assigned to a Status 5 - Severity 1 during the Analysis Period (AP)
1/2007. In the next 12 month period, AP 1/2008, the individual progressed two severity levels to a Status 5 –
Severity 3. During the AP-6/2008, the same individual would be grouped to another group, Status 6 –
Severity 2 as he/she had an additional chronic diagnosis of Congestive Heart Failure (CHF). As time goes
on, the graph shows that this individual again progressed to another group, Status 7 – Severity 4, as this
member now had another chronic diagnosis of Chronic Obstructive Pulmonary Disease (COPD).
CRGs Foundation for BCI-DSS Solution
BCI-DSS will enable BMS to follow a systematic process to understand the health status of its members and
population through the retrospective analysis of their experience. BCI-DSS leverages the CRGs, a
categorical clinical model that assigns each individual to a single, mutually exclusive severity adjusted
category (one of 1,073), the Clinical Risk Group (CRG). Each CRG represents a disease burden category
and varies with both the severity of the chronic condition as well as the co-morbidities found.
Each unique CRG maps to an aggregate status, into one of nine health statuses, where each status may
contain multiple CRGs. These health statuses range from catastrophic conditions, such as a history of a
heart transplant, to healthy individuals without either chronic health problems or indications of risk.
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 121
Figure 10-4, Clinical Risk Group Strata, and Figure 10-5, Population’s Health Status and Severity
Distribution, summarize the available CRG groupings.
Figure 10-4. Clinical Risk Group Strata.
The stratification shows which diseases are the most prevalent, which diseases have the highest burden, as
well as which diseases are projected to have the highest burden. Each BCI-DSS analytical report can be
filtered to provide the detailed information to both guide decision making and set the strategic directions.
Figure 10-5. Population’s Health Status and Severity Distribution.
Through the understanding of each individual’s health status, various clinical and business analyses and
studies can be conducted to provide the picture as to the state of the individual members and population with
respect to:
The disease burden
Disease progression
Resource consumption (cost and utilization) outliers
Profile utilization patterns
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 122
Track quality of care
Analyze the clinical efficacy of specific treatment patterns
Analyze the costs associated with specific medical services
Assess the appropriateness of payment levels
Set and review payment rates
Profile providers
CRG Assignment
The assignment process is a hierarchical open logic process, there is no black box, wherein each individual
is assessed based on his/her most significant diagnosis or diagnoses. The process of identifying those
diseases is conditional, relying on rules governing the presence and use of diagnoses, procedures,
pharmaceutical, and age/sex demographic factors (excluding costs).
This process utilizes readily available data that will be captured in the data warehouse, which is routinely
gathered as part of the processing of medical claims (e.g., ICD-9/10, CPT, HCPCS, etc.), and it also follows
uniform data standards, NCVHS, and National Drug Codes (NDCs), e.g., clinical drug nomenclature. CRGs
assign each individual member to a single CRG category.
CRGs have four key features.
Categorical Model. CRGs assign individuals to one and only one category. If multiple chronic diagnoses
are present, they are addressed either through severity adjustment of the most significant diagnosis or
through assignment to a CRG which includes multiple diagnoses (e.g., co-morbidities).
Severity Adjusted. All chronic diagnosis CRGs are severity adjusted and reflect the extent and
progression of the member’s diagnosis or diagnoses.
Hierarchical. All CRG assignments rely on hierarchical decisions. This assures that criteria are
consistently applied.
Conditional. CRGs make extensive use of conditional relationships, including recency and frequency
between and among diagnoses and procedures. This permits the recognition of precise clinical
relationships.
Benchmark Data and Predictive Modeling Weight Sets
3M’s CRGs is a predictive modeling software that builds upon the clinical categorization and stratification of
the specific population, as described above. Because the clinical logic focuses on the population’s disease
burden, the longitudinal progression of a member’s disease burden is easily supported. This disease
progression not only provides an understanding of the population’s burden, but it can be used to develop
expected values based on historical trending.
These expected values, known as relative weights, are associated with each CRG and by extension to each
individual assigned to that CRG. This in turn allows a case mix and severity adjusted index to be calculated
for any group of members which is a composite of the relative risk posed by all of the members of that group
and communicates the health status of the group as a whole. Relative weights and case mix indices can be
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 123
calculated for different combinations of benefits, resources, or other measures. Because CRG assignments
are based solely on clinical rather than statistical criteria, risk group assignments are independent of the
resources or other measures being projected.
Relative weights are important as they convey normative information for each risk category and can be used
to make predictions. Other factors, however, may also influence the predictive accuracy of relative weights,
including:
Age and sex, especially for the healthier segments of a population;
The extent of limited or partial exposure data for newer enrollees;
The age of the data or the difference between the period from which it is collected and the period for which
it is to be applied; and
Other factors such as differences in benefit design.
Since CRGs are a clinical model, these factors were intentionally excluded from the model.
We leverage the wealth of information BMS has in its members’ historical data. There is nothing more
valuable than creating normative data and benchmarks from the population you are actually analyzing, as it
will be automatically adjusted to the demographics and case mix of the West Virginia demographics.
To develop both the retrospective normative data sets, for benchmarking, and the prospective weight set, for
predictive modeling, we will use 2 years (years 2 and 3) of the initial 3 years of historical data, to include all
claim and encounter diagnosis and procedure codes, pharmacy NDCs, and member eligibility along with age
sex factors. The 2 year grouping period will stratify every member into his/her respective CRG category.
From this we will then map the aggregate resource consumption and financials in the second year of the
grouping period to develop the retrospective benchmark data sets. The prospective weight set will use the
same grouping mentioned above but will use the fourth year of data, i.e., most current year acquired, to
capture the aggregate resource consumption and financials, as shown in Figure 10-6. Prior to developing the
actual data sets, we will determine and trim the high and low outliers to provide a more accurate normative
data set for benchmarking and predictive modeling.
Figure 10-6. Weight Development Periods.
Deloitte will also work with BMS to identify any other types of normative data it would like to use to
benchmark its analyses against. During the initial phases of data processing, we will develop a core set of
prospective weights, i.e., relative risk scores, which will be used to benchmark. If BMS requires additional
normative data sets or have existing sets, we can load them into the data warehouse and use them to as
benchmarks.
WV_DW_DSS-065
Grouping Period
Resource Period
Yr-2 Yr-3
Grouping Period
Yr-3 Yr-4Yr-2
Resource Period
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 124
In addition to the relative weights developed using the CRGs, most defined analyses will have the ability to
compare performance to the respective aggregates, shown in Figure 10-7. Throughout the analyses, as the
users drill down into lower levels of detail, they will have the ability to compare against higher, aggregate
level detail.
Figure 10-7. Comparison of Subpopulations to the Total Population.
Data Refreshes and Updates
The BCI-DSS i-Portal will provide access to the complete set of data
stored in the data warehouse, a maximum of 10 years scrolling, where
end users will have the ability to run defined reports, to conduct ad hoc
queries, and to establish trending analysis against as much data as is
stored. After the initial load of 4 years of data, i.e., 3 historic and 1
current, every update will include the transactions processed since the
last load/update. Deloitte is currently planning to both load and
reconcile new refreshes of data on a monthly basis, but we can adjust
this accordingly based on the business requirements.
The i-Portal Dashboard (shown in Figure 10-8 below), which can be
configured based on user preferences, will view the detailed
information in the last 12 month period, referenced as the Analysis
Period (AP). This AP will be a scrolling 12 months and will be updated
on a monthly basis as new data extracts are received. Once new data
extracts are received and processed through the data cleansing and
validation process, it will then be processed through the business and
clinical processing logic. Once the data processing is complete, we will
then populate the respective data fields, by month, into the data
warehouse and refresh any required views in the BCI-DSS system.
ICD-10 Compliance
The implementation of ICD-10 in 2013 is likely the most significant event since the adoption of DRGs for
Medicare payments in 1983. ICD-10 codes bring more clinical specificity and precision. Healthcare
organizations are faced with finding and translating all ICD-9 codes in their documentation‚ contracts‚
homegrown applications‚ databases‚ financial and quality reports‚ super-bills‚ and medical necessity policies.
The question is: Can this process be fully automated?
WV_DW_DSS-064
Population Mem Avg MM Avg DB DB ImpactAvg Cost
(PMPM)
Proj Cost
(PMPM)
Total 331,318 7 1.124 112.380 $531 $846
FFS 218,301 8 1.562 102.920 $762 $1,250
MCO 1 113,017 7 0.277 9.46 $565 $1,169
A Dictionary is Handy!
The GEMs are like a two-way
foreign language dictionary. You
can look up an ICD-9 code and
see all the ways it might be said
in ICD-10. You use the 9-to-10
GEM if you only know the ICD-9
code and are forced to use the
(usually worse) ICD-10 code that
expresses it. You use the 10-to-9
GEM (in reverse lookup, a
capability not found in non-
computerized dictionaries) to see
all the ways an ICD-10 coder
might code the condition or
procedure in ICD-10 when he/she
has access to additional
information in the medical record.
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 125
Deloitte has been researching and working with industry leaders to identify sustainable approaches and to
develop methodologies to ease the transition. Our experts have been working with many commercial payers,
providers and integrated delivery systems, as well as state Medicaid agencies, to develop the ICD-10
transition roadmaps and implementation plans to train resources and remediate existing system and
business processes.
Deloitte has teamed with some of the leading clinical and coding experts in the industry to include 3M HIS.
No entity has more experience with ICD-10s than 3M HIS, as it is under contract with the Center for
Medicare & Medicaid Services (CMS) as they designed and developed the ICD-10 Procedure Coding
System (PCS) and the General Equivalence Mappings (GEMs).
3M HIS has a plan in place to update all their groupers that use ICD diagnosis and procedure codes, which
includes the CRGs, by October 2012. 3M’s approach is to replicate the ICD-9 based groupers in ICD-10,
which means the same record will generate the same CRG whether it is ICD-9 or ICD-10 based. In time, as
post implementation data is generated and can be used to calibrate 3M’s logic, the groupers will begin to
take advantage of the new level of specificity.
This plan, which is a sound and conservative way of dealing with the transition, will have the added benefit of
allowing organizations to continue to track their data trends without running into a sudden cliff caused by the
shift in member assignments that will be created by the integration of new data.
Using the CRGs to clinically stratify the members will minimize the effects, caused by the ICD-10 transition,
on the BCI-DSS analyses, historical trends and studies, as the CRGs will level off any sudden shifts in
trends.
BCI-DSS Analytics
Deloitte’s proposed BCI-DSS solution is designed to exceed BMS’ DW/DSS requirements as outlined in the
RFP. Within our solution and the underlying data model, with the business and clinical stratification, lies the
foundation for BMS to design, conduct, and develop an unlimited number of defined reports, trending
analyses and ad hoc queries. Users will be trained to understand the data foundation after which they will be
able to design and develop their own reports, analyses and run various ―what-if‖ queries through the use of i-
Portal, a complete COGNOS system solution. Using COGNOS as the underlying application for the i-Portal
will enhance BMS’ capability to support any type of informational analysis.
In addition to the power and flexibility of the i-Portal, Deloitte brings a wealth of cross operational expertise
and subject matter experts to BMS to advise on the development of data analytics for this engagement. We
also bring a wealth of experienced advisors with relevant qualifications in all aspects of business and clinical
analytics, to include, but not limited to:
Financial analysis and management
Program performance analysis
Risk-adjustment
Medicaid rate setting
Provider performance monitoring
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 126
Provider performance analysis (P4P)
Quality management and assessments
Population analysis
Resource consumption outlier analysis
Profile utilization patterns analysis
Quality of care analysis
Analyze the clinical efficacy of specific treatment patterns
Analyze the costs associated with specific medical services
Assess the appropriateness of payment levels
Set and review payment rates
Profile providers
The combination of our professionals’ reporting expertise and the awareness of the relevant analytics to
support BMS’ goals will provide valuable insight into the development of the BCI-DSS analytical solution.
Deloitte’s solution is comprehensive and will power BMS and their work units and business areas with
actionable, intelligent information delivered through the use of an innovative leading edge BCI-DSS system
that is flexible and which will surpass the current set of demands, thereby leading BMS into the MITA
Oriented future.
Analytics Delivery
Deloitte’s focus during phase one is to compile a comprehensive list of reports to develop in support of BMS’
work units/business areas. We will work with BMS to prioritize the list of reports in order to develop a delivery
plan. These reports will then be further defined as we work with the various work units to detail the design
specifications. Each report design will include (i) a layout of the report with drilldowns, if applicable; (ii)
specifications of the data elements required to produce the report; and (iii) the business and clinical
mathematics required to generate the report. Each report design will be reviewed and signed off before
moving into the development life cycle.
Deloitte’s approach will develop the analytics for tiered users, to include executive, causal business, and
power level users. When users log into the i-Portal they will have a customizable dashboard which they can
configure to meet their specific requirements, as shown in Figure 10-8. There will be a set of defined
modules that a user can select to view. A user will be granted access or entitlement authorizations that will
define both the level of detail and the business areas he/she is authorized to view.
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 127
Figure 10-8. i-Portal Dashboard.
From the Dashboard, shown in Figure 10-8, users will have access to the respective areas they are entitled
to access. An extensive portfolio of defined reports will be available through the i-Portal. The reports serve as
templates that can be either run as is or modified by the user. The reports are organized in topical folders
that cover a wide range of information by business area, for example, Care Management will have topical
folders for Population, Member, Utilization, Quality, Outcomes, Trending/Studies along with an ad hoc report
generation function. Within each topical folder there will be a series of defined reports and studies/trending
analyses. Each report will have various filters to focus the information viewed as well as providing drilldown
capabilities into the next levels.
BCI-DSS will have significant filtering capabilities:
Analysis/Date Period
Product (e.g., FFS, MCO1, MCO2, MCO3)
Eligibility
Eligibility Category
Region/County
Demographics (e.g., age, gender)
Disease
Services (e.g., utilization types, service type, sites of service)
Quality Indicators (i.e., gaps in care, outliers,)
Provider Types
View
Trend Analyses
View Detailed
Analytical ReportsList of Frequently
Used / Accessed
Analytical Reports
Establish Alerts and Triggers
Geo Map Data
WV_DW_DSS-058
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 128
Claim Types
Pharmacy Types (e.g., brand, generic)
As the user drills down in the reports, he/she will be able to benchmark the detail to the aggregate(s). The
below figures demonstrates the drilldown and benchmarking capabilities.
The Geo-Mapping capability, inherent in the COGNOS tool, allows the user to map the information analyses
to a geographic map in order to provide a graphic view of the information distribution. This provides the basis
to identify key areas by geographical component (i.e., region, county, zip code, etc.).
Personal Health Record
BCI-DSS will enable BMS to follow a systematic process to understand the health status of its members and
population through the retrospective analysis of their experience. The data warehouse design centers on the
members’ data from which everything else is derived; therefore the lowest level of information is the member
level detail which can be viewed in their Personal Health Record (PHR).
A crucial part of a business and clinical analytical solution is to create a clear picture of a member’s health
status. BCI-DSS does just that, through the creation of a member PHR, as shown in Figures K1 through K6,
by aggregating healthcare claims, pharmacy, lab results and other various sources of data. The underlying
goal of the PHR is to capture each individual’s health status and to share this information across the
healthcare delivery spectrum.
The PHR contains a series of tabs whereby users with the proper entitlements can view the latest 12 month
analysis period of claims data (i.e., medical, encounters and pharmacy), clinical and financial details, and
quality indicators. The PHR is the lowest level of drilldown from a member list, as shown in the analytic
examples below. During the business requirements gathering effort, all elements in the PHR tabs will be
reviewed for potential revision and validation before development.
The PHR contains the following tabs:
Administrative. This tab contains the individual’s administrative information to include contact information,
age, sex, eligibility months, and Primary Care Physician (PCP) information. (Shown in Appendix A, Sample
Reports).
Claims. Included in this tab is a chronological list of processed medical claims/encounters to include all
inpatient, outpatient, office, ambulatory surgical center, emergency room, and independent laboratory as well
as pharmacy. Claims will also be able to be filtered by claim type. (Shown in Figure 10-9).
Providers. This tab displays a chronological list of providers that have provided services to the member. It
identifies the provider specialty, date of service, place of service, diagnoses, and procedures. (Shown in
Figure 10-10).
Pharmacy. A listing of all Rx prescriptions filled by the individual. This list contains the NDC, description, fill
date, brand/generic, number of fills, prescribing provider, pharmacy and cost. (Shown in Figure 10-11).
Diagnoses and Procedures. This tab contains the list of diagnoses and procedures categorized by disease
category. (Shown in Appendix A, Sample Reports).
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 129
Admits. This tab contains a list of inpatient admissions that the member has had. The information includes
admit and discharge dates, attending physician, diagnoses, procedures, Length of Stay (LOS), total paid and
complications, DRG (if assigned). As an option, but not scoped for inclusion in this proposal, BMS could use
the 3M’s All Patient Refined DRGs (APR™ DRGs) to view the APR-DRG, APR-SOI (Severity of Illness), and
APR-Risk of Mortality (ROM), cost variance, and LOS variance. (Shown in Appendix A, Sample Reports)
Diagnostic Testing. This tab includes three lists; laboratory results, radiology results and diagnostic
procedure results. The information displayed in this tab is populated by data extraction from ancillary service
data (e.g., national providers of diagnostic testing services). The information captured includes test type,
description, dates of service, results, and abnormal flags (lab only). (Shown in Figure 10-12).
History. This tab provides the details as to the member’s health status/disease history and his/her
progression over time and will include the AP date range, CRG (Health Status) status and severity, disease
description, disease burden, total paid, projected cost, inpatient admits, and number of gaps in care. (Shown
in Figure 10-13).
Summary. This tab details the various utilization, quality, financial, pharmacy, as well as identified gaps in
care. (Shown in Figure 10-14).
Figure 10-9. PHR Claims Detail Tab.
WV_DW_DSS-101_4
IP Cost OP Cost OP Surgery Cost Rx Cost Lab Cost Rad Cost Ther Cost
$25,594 $18,243 $68 $9,469 $554 $474 $40
Date of
ServicePlace of Service Description Units
Primary
Diagnosis
Secondary
Diagnosis
Total
CostNetwork Provider Name
09-Apr-2006 Emergency Room Emergency Dept Visit 1 Migrne Unsp w/o Ntrc Mgrn $23 Y Powell, Ralph
09-Apr-2006 Emergency Room Emergency Dept Visit 1 Migrne Unsp w/o Ntrc Mgrn $137 Y Powell, Ralph
09-Apr-2006 OP Hospital Hydroxyzine Hcl Injection 1 Migrne Unsp w/o Ntrc Mgrn $17 Y Powell, Ralph
09-Apr-2006 OP Hospital Meperidine/Promethazine Inj 1 Migrne Unsp w/o Ntrc Mgrn $6 Y Powell, Ralph
09-Apr-2006 Pharmacy Albuterol 17 $13 Y Johnson, Robert L
09-Apr-2006 Pharmacy Zofran 50 $1,119 Y Johnson, Robert L
03-Apr-2006 Emergency Room Emergency Dept Visit 1 Migrne Unsp w/o Ntrc Mgrn $19 Y Powell, Ralph
03-Apr-2006 Emergency Room Emergency Dept Visit 1 Migrne Unsp w/o Ntrc Mgrn $137 Y Powell, Ralph
03-Apr-2006 OP Hospital Hydroxyzine Hcl Injection 1 Migrne Unsp w/o Ntrc Mgrn $17 Y Powell, Ralph
03-Apr-2006 OP Hospital Meperidine/Promethazine Inj 1 Migrne Unsp w/o Ntrc Mgrn $6 Y Powell, Ralph
01-Apr-2006 Pharmacy Catapres-Tts 3 4 $165 Y Johnson, Robert L
30-Mar-2006 Office Office/OP Visit, Est 1 DM 2 w/o Cmp Nt St Uncntr $23 Y Johnson, Robert L
30-Mar-2006 Pharmacy Hydrocodone-Acetaminophen 25 $9 Y Johnson, Robert L
Admin Claims Providers Pharmacy Diagnoses and Procedures Admits Diagnostic Testing HS History Summary X
Member ID: 1191167 Disease Burden: 4.530
Name: Smith, Jane R Disease(s): Diabetes and Asthma
DOB: 04-Oct-1972 CRG S/S: 6-5
Sex: F Cost: $53,305 Projected Cost: $42,382
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 130
Figure 10-10. PHR Provider Service Detail Tab.
Figure 10-11. PHR Pharmacy Detail Tab.
WV_DW_DSS-102_4
Provider ID Provider Name PCP Specialty Network Date of Service Place of Service Primary Diagnosis Secondary DiagnosisPrimary
Procedure
Secondary
ProcedureP
710480302 Powell, Ralph J No Y 01-Feb-2006 IP Hospital DM 1 Neuro Nt St Uncntrld Gastroparesis
207844200 Johnson, Robert R Yes Gen Practice Y 01-Feb-2006 IP Hospital Nausea with vomiting DM 1 w/o Cmp Nt St Uncntrld Initial Hospital Care
800566513 Powell, Ralph No Y 01-Feb-2006 Emergency Room Nausea with vomiting Abdominal Pain Epigastric
800566513 Powell, Ralph No Y 01-Feb-2006 Emergency Room Nausea with vomiting Abdominal Pain Epigastric Als1-Emergency
710480302 Powell, Ralph J No Y 02-Feb-2006 IP Hospital Abdominal Pain Oth Spcf St CT Abdomen w Dye
710480302 Powell, Ralph J No Y 02-Feb-2006 IP Hospital Abdominal Pain Oth Spcf St CT Pelvis w Dye
207844200 Johnson, Robert R Yes Gen Practice Y 02-Feb-2006 IP Hospital Nausea with vomiting DM 1 w/o Cmp Nt St Uncntrld Subsequent Hospital Care
207844200 Johnson, Robert R Yes Gen Practice Y 03-Feb-2006 IP Hospital Nausea with vomiting DM 1 w/o Cmp Nt St Uncntrld Subsequent Hospital Care
207844200 Johnson, Robert R Yes Gen Practice Y 16-Feb-2006 Office DM 1 Neuro Uncntrld Gastroparesis Office/OP Visit, Est
207844200 Johnson, Robert R Yes Gen Practice Y 21-Feb-2006 Office Acute Sinusitis Nos Office/OP Visit, Est
010566503 Powell, Ralph No Y 01-Mar-2006 OP Hospital Headache Meperidine/Promethazine Inj
010566503 Powell, Ralph No Y 01-Mar-2006 Emergency Room Headache
010566503 Powell, Ralph No Y 01-Mar-2006 OP Hospital Headache Hydroxyzine Hcl Inj
010566503 Powell, Ralph No Y 01-Mar-2006 Emergency Room Headache Emergency Dept Visit
Admin Claims Providers Pharmacy Diagnoses and Procedures Admits Diagnostic Testing HS History Summary X
Member ID: 1191167 Disease Burden: 4.530
Name: Smith, Jane R Disease(s): Diabetes and Asthma
DOB: 04-Oct-1972 CRG S/S: 6-5
Sex: F Cost: $53,305 Projected Cost: $42,382
WV_DW_DSS-103_4
NDC Number Description Fill Date Brand Name (Y/N) Quantity Fills Provider Name Cost Pharmacy Name
006033881 Hydrocodone-Acetaminophen 30-May-2006 Y 25 4 Johnson, Robert L $37
575999877 Precision Xtra 30-May-2006 Y 50 18 $1,629
005970033 Catapres-Tts 3 26-May-2006 Y 4 3 Johnson, Robert L $495
575998547 Precision 26-May-2006 Y 8 5 $115
599301560 Albuterol 26-May-2006 N 17 13 Johnson, Robert L $186
000027510 Humalog 26-May-2006 Y 10 10 $789
001730446 Zofran 22-May-2006 Y 50 3 Johnson, Robert L $3,401
003783475 Nifedipine Er 22-May-2006 Y 30 7 Johnson, Robert L $262
370000455 Prilosec Otc 22-May-2006 Y 28 11 Johnson, Robert L $295
501110430 Metoclopramide Hcl 22-May-2006 N 60 14 Johnson, Robert L $178
003782074 Lisinopril 19-May-2006 N 60 8 Johnson, Robert L $125
006035468 Propoxyphene Napsylate w/Apap 19-May-2006 Y 65 18 $309
000930864 Ciprofloxacin Hcl 16-May-2006 N 16 1 $10
007812613 Amoxicillin 15-May-2006 Y 30 6 $59
Admin Claims Providers Pharmacy Diagnoses and Procedures Admits Diagnostic Testing HS History Summary X
Member ID: 1191167 Disease Burden: 4.530
Name: Smith, Jane R Disease(s): Diabetes and Asthma
DOB: 04-Oct-1972 CRG S/S: 6-5
Sex: F Cost: $53,305 Projected Cost: $42,382
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 131
Figure 10-12. PHR Diagnostic Testing Results Tab.
Figure 10-13. Health Status History Tab.
Lab
Test Type Date of Service Result Range
HGB A1c 17-Aug-2006 4.8 14.0-18.0
HBG 17-Aug-2006 L7.0* 42.0-52.0
HTC 17-Aug-2006 L21.1 4.3-6.1
Radiology
Test Type Date of Service Result Range
Diagnostic Procedures
Test Type Date of Service Result Range
Key L = Abnormal Low, H = Abnormal High, WNL = Within Normal Limits, * = Critical Value
WV_DW_DSS-106_3
Admin Claims Providers Pharmacy Diagnoses and Procedures Admits Diagnostic Testing HS History Summary X
Member ID: 1191167 Disease Burden: 4.530
Name: Smith, Jane R Disease(s): Diabetes and Asthma
DOB: 04-Oct-1972 CRG S/S: 6-5
Sex: F Cost: $53,305 Projected Cost: $42,382
WV_DW_DSS-107_4
Analysis Period CRG-SS CRG Description DB Total CostProj Cost
(PMPM)IP Adm Gaps In Care Product
June 2005 to May 2006 6-5 Diabetes and Asthma 4.530 $53,305 $42,382 10 3 FFS
June 2004 to May 2005 6-3 Diabetes and Asthma 3.890 $32,521 $36,859 5 6 FFS
Admin Claims Providers Pharmacy Diagnoses and Procedures Admits Diagnostic Testing HS History Summary X
Member ID: 1191167 Disease Burden: 4.530
Name: Smith, Jane R Disease(s): Diabetes and Asthma
DOB: 04-Oct-1972 CRG S/S: 6-5
Sex: F Cost: $53,305 Projected Cost: $42,382
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 132
Figure 10-14. PHR Member Experience Summary Tab.
Inpatient Parameters AP Value
Admits 4
Days 18
Actual/Expected LOS 0
Outpatient Parameters AP Value
ER Visits 29
OP Hospital Visits 0
Office Visits 12
Lab Tests 53
Radiology 13
Therapy 1
Consults 10
Quality Parameters AP Value
Preventable Admits 0
Gaps In Care 3
Re-admits < 30 days 0
Disease Progression 0
Disease Complication 0
Financial Parameters AP Value
Total Cost $53,305
Inpatient $25,594
Outpatient $18,243
Hospital Outpatient $5,232
Lab $554
Radiology $474
Therapy $40
Office $271
Pharmacy Cost $9,469
Out of Network $0
Pharmacy Parameters AP Value
Total Scripts 62
Brand Scripts 41
Projected/Actual Cost Parameters AP Value
Projected Cost (PMPM) $3,532
Actual Cost (PMPM) $4,442
Projected / Actual Cost Ratio 0.8
Gaps In Care
Asthma Pharmacotherapy
Influenza Vaccine
Pneumonia Vaccine
Admin Claims Providers Pharmacy Diagnoses and Procedures Admits Diagnostic Testing HS History Summary X
WV_DW_DSS-108_3
Member ID: 1191167 Disease Burden: 4.530
Name: Smith, Jane R Disease(s): Diabetes and Asthma
DOB: 04-Oct-1972 CRG S/S: 6-5
Sex: F Cost: $53,305 Projected Cost: $42,382
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 133
Operations Management
Operations Management is the focal point of the Medicaid enterprises. It includes the payment to providers,
Managed Care Organizations (MCOs), pharmacy program, etc. This business area focuses on payments
and receivables and owns all information associated with service payment and receivables.
The DSS analytics will focus on service utilization, service deliver and service financials. These information
analyses will be developed through easily accessible series of defined reports along with the ability to create
and run ad hoc queries.
The core Operations Management need of any Medicaid agency is insight into the utilization and financial
picture. Many systems will provide lists of claims for summing costs; i-Portal does much more, by showing
the reasons for cost trends. It is designed to return accurate information that can be used to see the holistic
view of the entire Medicaid operation. Some of its financial reporting capabilities encompass the following:
Financial analyses to match utilization to costs from the entire population view down to the individual
member and provider.
Hundreds of pre-calculated and customizable measures, such as sums, rates and ratios (per member,
PMPM, per 1000, per admit).
Benchmark to aggregate information to perform comparison analyses.
Utilization analyses to view resource consumption across populations.
The following reports demonstrate the level of detail designed in the BCI-DSS solution. These reports
generally provide an overall view of the population level but can be filtered to focus the information down to
the detail level. These reports support the drilldown to the next level of detail to the individual personal health
record.
Figure 10-15. Population Cost Overview by Health Status Category.
Figure 10-15 demonstrates the BCI-DSS ability to provide a financial overview of the population by health
status. This report shows the cost on both a per member per month (PMPM) and per member per year
(PMPY) basis, but links it to the clinical component of the data. This report can be run against any population
through the filter settings. Users will have the ability to view this report by defined subpopulations (i.e., FFS,
MCOs, products, eligibly members, age categories, regions, etc.). This report has drill down capabilities,
Health Status Mem % Mem Avg MM Cost
(PMPM) Cost
(PMPY) Proj Cost (PMPM)
Proj Cost (PMPY)
Avg DB
Total Population 331,318 100 7 $531 $3,966 $682 $8,182 1.124
1 Healthy 185,858 56.10 7 $49 $322 $54 $647 0.173
2 Significant Acute 10,555 3.19 7 $236 $1,723 $196 $2,356 0.316
3 Single Minor Chronic 18,670 5.64 7 $203 $1,479 $216 $2,593 0.499
4 Multiple Minor Chronic 5,088 1.54 8 $341 $2,621 $432 $5,185 0.840
5 Single Significant Chronic 43,679 13.18 8 $518 $4,178 $734 $8,813 1.340
6 - Two Significant Chronic 43,283 13.06 9 $1,277 $12,111 $2,138 $25,662 3.190
7 Multiple Significant Chronic 17,118 5.17 10 $2,024 $19,623 $3,452 $41,424 5.133
8 Complex Malignancies 3,210 0.97 8 $2,178 $17,744 $2,098 $25,174 3.990
9 Catastrophic 3,857 1.16 9 $2,703 $24,808 $4,436 $53,228 6.748
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 134
where the user can see the financial breakdown at a granular level, i.e. severity levels, as well as drill downs,
as shown in Figures 10-16.
Figure 10-16. Expansion to View Severity Level Details.
Deloitte, through the BCI-DSS solution, is not only producing the reports but the data files that underlie the
reports. During the business requirements gathering effort, we will work with the business area users and
determine the types of specific reports they require to develop as defined queries. We will also train the
users and enable them to conduct their own analytics, so that users can conduct ―what-if‖ analyses and other
querying and reporting on the data. During the requirements gathering we will review a list of MARS reports
that BMS would like to have in the BCI-DSS system, for post claims adjudication analysis.
BMS has identified in the RFP that there are approximately 300 MARS reports they would like to transition
over to the new DSS system. In reviewing the list of these reports, we have come to the opinion that many of
these static reports can be redesigned and reprogrammed to provide a more innovative and dynamic
delivery of the required information to the end users. Deloitte will work with BMS to design the post claims
adjudication MARS reports in our BCI-DSS solution in order to minimize the number of reports to be
developed, while still providing the required information to guide decision making and guide operations.
Care Management
Care Management is the most advancing business area as the Medicaid program evolves. Care
Management includes the processes that support both individual care management and population
management. This area collects information about the needs of the individual members, their health status,
plans of care, and outcomes. It is focused on identifying members that require special needs, assesses
needs, develops treatment plans, monitors and manages the plan, and reports outcomes.
The BCI-DSS analytics will focus on the population and member health status, quality indicators, service and
resource utilization, and outcomes analysis. The DSS will provide a Patient Health Record that provides a
detailed view of member’s service-level detail information.
Health Status Mem % Mem Avg MM Cost
(PMPM) Cost
(PMPY) Proj Cost (PMPM)
Proj Cost (PMPY)
Avg DB
Total Population 331,318 100 7 $531 $3,966 $682 $8,182 1.124
1 Healthy 185,858 56.10 7 $49 $322 $54 $647 0.173
2 Significant Acute 10,555 3.19 7 $236 $1,723 $196 $2,356 0.316
3 Single Minor Chronic 18,670 5.64 7 $203 $1,479 $216 $2,593 0.499
4 Multiple Minor Chronic 5,088 1.54 8 $341 $2,621 $432 $5,185 0.840
5 Single Significant Chronic 43,679 13.18 8 $518 $4,178 $734 $8,813 1.340
Severity Level 1 29,969 9.05 8 $377 $2,971 $519 $6,224 1.038
Severity Level 2 8,228 2.48 8 $572 $4,767 $882 $10,589 1.479
Severity Level 3 3,619 1.09 9 $985 $8,625 $1,579 $18,947 2.622
Severity Level 4 974 0.29 9 $1,366 $11,861 $1,897 $22,758 2.909
Severity Level 5 491 0.15 8 $1,107 $8,557 $1,410 $16,916 2.539
Severity Level 6 398 0.12 9 $2,082 $18,235 $2,561 $30,733 4.236
6 - Two Significant Chronic 43,283 13.06 9 $1,277 $12,111 $2,138 $25,662 3.190
7 Multiple Significant Chronic 17,118 5.17 10 $2,024 $19,623 $3,452 $41,424 5.133
8 Complex Malignancies 3,210 0.97 8 $2,178 $17,744 $2,098 $25,174 3.990
9 Catastrophic 3,857 1.16 9 $2,703 $24,808 $4,436 $53,228 6.748
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 135
Our solution for care management analytics has been developed by a wealth of experienced clinical and
analytical experts that have worked with commercial, state Medicaid and federal Medicare operations. Our
experts bring experience in providing targeted interventions and supporting continuously measured
outcomes, integrating medical management initiatives, creating a holistic and longitudinal view of the
member across the continuum, allowing the clinical care manager to engage members and providers, and
providing a modular approach to promote continuous quality/performance improvement. Below are a few
examples of our clinical reporting.
Our BCI-DSS solution will enable end users with a wealth of analytical reports and drilldown capabilities.
BCI-DSS builds on the understanding of each member’s health status, to create the underlying foundation for
the population analyses. These analyses aggregate at the member level and group individuals by clinically
meaningful categories, based on their disease or combination of diseases (co-morbidities), state of
progression (disease burden), and severity of illness (e.g., clinical complexity). Such grouping creates a
common, clinically based foundation of comparative data that can be used as the basis for effective decision
support and program management.
Through the population’s clinical stratification, users can easily identify the diseases and combinations of
diseases that are placing the highest burden on BMS. Figure 10-17, Top 5 Diseases by Disease Burden
Impact, illustrates population grouping and reporting according to highest impact statistics. Figure 10-17
displays the top five diseases that are placing the highest burden on the specific population.
For each disease, we case mix and severity adjust the members categorized with chronic diseases to
develop an individual disease burden score (Avg DB). As we look for the diseases that are placing the
highest burden, we weight the average disease burden with the percent of the population categorized as
actually having the chronic diseases. For example, in Figure 10-17, Diabetes has an Avg DB of 3.0, where
Congestive Heart Failure (CHF) has an Avg DB of 3.7. Just because CHF has a higher Avg DB than
Diabetes does not mean that the burden to the state is more significant. To determine the direct impact, one
has to look at the number of individuals that actually have the chronic disease; therefore, Diabetes which has
more members (69,404) than CHF (24,558) has a higher Disease Burden (DB) Impact.
Figure 10-17. Top 5 Diseases by Disease Burden Impact.
Similarly, Figure 10-18, Diabetes Drilldown Reflecting Top 15 Co-morbidities by Disease Burden Impact,
shows the next level of detail available by drilling down into the Diabetes disease category.
Population Mem MM Avg DB DB Impact
Total 1,228,893 12,390,712 1 100
Disease Mem MM Avg DB DB Impact
Schizophrenia 69,404 758,032 3.0 16.9
Diabetes 58,108 644,147 2.2 10.4
Asthma 96,401 1,081,159 1.2 9.4
COPD and Bronchiectasis 30,717 343,094 3.3 8.2
Congestive Heart Failure 24,558 261,594 3.7 7.4
WV_DW_DSS-050_2
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 136
Figure 10-18. Diabetes Drilldown Reflecting Top 15 Co-Morbidities by Disease Burden Impact.
From this information, BMS can both identify the diseases for primary
focus and develop the programs and interventional strategies to
address the impacts. For example, when reviewing the diseases that
are placing the highest burden on the state, e.g., Diabetes, one can drill
down into each specific disease and determine the burden drivers. This
drill down capability will allow BMS to determine if disease and/or case
management programs need to focus on the individual disease, such
as Diabetes alone, or whether it should focus on co-morbidities (e.g.,
Diabetes and Advanced Coronary Artery Disease (CAD)). This
understanding creates the basis to identify the appropriate individuals
for enrollment into various programs.
The BCI-DSS solution will also provide detailed analyses comparing
various populations to an aggregate benchmark. Figure 10-19 shows
the FFS and MCO1 populations’ dominating diseases compared
against the overall population. This analysis provides a view of not only
the dominant diseases but also provides the disease burden. For example, in Figure 10-19 it shows that
Schizophrenia is the dominant chronic disease for all 3 populations, but all three have a different disease
burden. The overall population has an average disease burden of 1.966 compared to FFS’s 2.759 and
MCO1’s 1.909. This means that the FFS has a sicker population of Schizophrenia then MCO1 and above the
total population benchmark. Another view of the analysis shows that MCO1 has a disease burden impact of
Total Mem MM Avg DB DB Impact
Total 1,228,893 12,390,712 1 100
Disease Mem MM Avg DB DB Impact
Diabetes 69,404 758,032 3.0 16.9
Diabetes 8,967 89,505 1.6 20.7
DM-Other Mo Chron 4,420 50,678 3.0 19.1
DM-Adv CAD-Oth Dom Chron 3,407 37,977 3.8 18.7
DM-HPT-Oth Dom Chronic 3,710 41,719 3.0 16.0
Diabetes – Advan CAD 3,710 40,312 2.9 15.5
CHF – Diabetes – COPD 2,242 24,672 4.2 13.6
DM – Other Sig Chronic 3,124 33,997 3.0 13.5
CRF-DM-Oth Dom Chron 1,878 19,471 4.4 11.9
CHF-Diabetes 2,245 23,600 3.5 11.3
Diabetes – HPT(1) 3,290 33,399 2.0 9.5
DM-1+ Other Dom. Chron Dis. 1,617 17,703 3.8 8.9
CHF-DM-Other Dom Chron 1,412 14,303 3.9 7.9
DM-Adv CAD-Oth Dom Chron (3) 1,340 15,151 4.1 7.9
DM-Adv CAD-Oth Dom Chron (4) 1,165 13,645 4.1 6.9
Diabetes – Asthma 1,632 18,922 2.7 6.3
WV_DW_DSS-010_2
Disease Burden
Using 3M’s CRGs as the
categorical classification model
provides the means to case mix
and severity adjust the members
that have common disease(s).
For example, not all Diabetics
are grouped in the same bucket,
they will be distributed to
separate buckets based on the
progression of their disease. So
when you case mix members
within a disease they will vary
in progression, i.e., the severity
adjustment.
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 137
1.366 for Schizophrenia, higher than the FFS and above the total population benchmark. This is due to the
fact that they have more members with Schizophrenia and this places a larger burden on them from a
resource consumption stand point. With this information, users can start to do more drill down analysis to
determine the cost associated with the impacts.
Figure 10-19. Population Comparison of Diseases and Disease Burdens.
Based on the detailed stratification of the data stored in the data warehouse, BMS operations and end users
will be able to query various detailed reports and trending analyses. The following are some examples of the
various member level reports that the care managers can view.
The BCI-DSS solution has 4 main focus areas, Quality, Utilization, Pharmacy, and Financial analyses. The
quality reports focus on quality of care and service deliver, looking at gaps in care, preventable inpatient
admissions and re-admissions. This enables care managers with the ability to effectively stratify the
Population Mem Avg MM Avg DB DB ImpactAvg Cost
(PMPM)
Proj Cost
(PMPM)
Total 331,318 7 1.124 112.380 $531 $846
Disease
Schizophrenia 254 8 1.966 0.153 $1,817 $2,030
Diabetes 120 8 1.550 0.062 $576 $1,156
Substance Abuse 106 9 .811 0.032 $331 $485
Hypertension 83 8 1.074 0.033 $324 $210
Asthma 367 8 .281 0.034 $297 $356
Population Mem Avg MM Avg DB DB ImpactAvg Cost
(PMPM)
Proj Cost
(PMPM)
FFS 218,301 8 1.562 109.920 762 $1,250
Disease
Schizophrenia 17 9 2.759 0.142 $1,367 $2,030
Hemi- and Quadriplegia 4 9 7.167 0.087 $6,784 $5,602
Diabetes 7 9 2.867 0.061 $631 $1,156
Asthma 13 5 0.578 0.023 $501 $356
COPD and Bronchiectasis 3 7 2.131 0.019 $485 $1,995
Population Mem Avg MM Avg DB DB ImpactAvg Cost
(PMPM)
Proj Cost
(PMPM)
MCO 1 113,017 7 0.277 9.46 $565 $1,169
Disease
Schizophrenia 237 8 1.909 1.366 $1,637 $2,030
Diabetes 113 8 1.468 0.501 $832 $1,156
Asthma 354 8 0.27 0.289 $302 $356
Hypertension 81 9 1.063 0.260 $289 $210
Substance Abuse 106 9 0.811 0.259 $331 $485
WV_DW_DSS-109_4
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 138
population and select appropriate members for intervention based on disease burden and indicators of
disease progression or risk factors of disease progression and/or complications.
Quality Analysis
Deloitte developed the gaps in care logic using external evidence based guidelines (e.g., HEDIS, CDC, ADA,
etc.). Our gaps in care focus on the following categories:
Screenings which includes Breast, Cervical and Colorectal Cancer, and Chlamydia;
Immunizations which includes all childhood and adolescent immunizations, pneumonia and influenza
vaccines; and
Disease Specifics which include Beta-blockers post MI, Lipid testing post Cardiovascular event,
Osteoporosis management post hip fracture, Asthma medication and Multiple Diabetes treatment gaps
such as vision screening, recency of Hgb A1c, foot exams, nephropathy screening and lipid testing.
The majority of these gaps are based on HEDIS requirements with the rest following CDC or ADA
recommendations. The design of our BCI-DSS is driven from dynamic configuration tables, so it easily
supports flexibility to add or modify gaps in care as BMS requires.
Figure 10-20. Member Quality Analysis.
Figure 10-20 shows a standard member quality analysis. This analysis can be filtered to view members
within certain subpopulations, as will be defined during the business requirements gathering. This analysis
not only shows the number of gaps in care, preventable inpatient (IP) admissions (Prev Admits) and re-
admissions (Re Admits) but also shows the progression of the member’s disease(s), in the Δ DB (delta
disease burden) column. For example, Jane Smith, a 68 year old female, has 6 gaps in care, 2 preventable
admits and 1 readmission. From the last analysis period to the current analysis period, she progressed in
disease burden by 4.523. This is an indicator that she may be at high risk for further progression. The
preventable admission could be caused by her not getting the appropriate level of care, as evident with the
gaps in care. To further investigate this potential case to figure out the root cause, a care manager could drill
down into this Jane’s PHR to view the details that led to the disease progression.
Member ID Name Product Age Sex CRG S/S
DB Δ DB MM Cost
(PMPY) Proj Cost (PMPY)
Gaps in Care Prev
Admits Re
Admits
17500663 Smith, George L FFS 62 M 7-4 5.319 0.000 12 $41,056 $49,765 5 6 4
14206214 Smith, Jane FFS 55 F 8-5 6.793 1.272 3 $62,781 $0 2 6 15
21579620 Smith, George L FFS 48 M 7-5 5.559 0.000 12 $64,952 $52,006 7 4 3
16000599 Smith, George C FFS 68 M 9-6 8.694 0.000 12 $20,193 $81,342 8 1 7
04882818 Smith, Jane J FFS 48 F 9-4 7.016 3.549 12 $160,815 $65,641 11 4 7
12844850 Smith, Jane MCO 2 63 F 7-4 5.281 0.000 12 $15,467 $49,406 4 2 2
24004517 Smith, Jane FFS 47 F 7-4 6.016 0.000 12 $23,892 $56,282 7 2 2
03936377 Smith, Jane R MCO 2 68 F 7-3 4.523 2.658 9 $15,547 $42,314 6 2 1
61733632 Smith, Jane S MCO 1 4 F 5-1 0.11 0.000 12 $750 $0 8 0 3
16330566 Smith, George T FFS 64 M 7-5 6.732 0.000 12 $25,824 $62,981 8 3 2
32006563 Smith, George MCO 1 79 M 9-6 21.135 0.000 12 $13,705 $197,734 4 3 5
62457113 Smith, George W MCO 1 65 M 7-4 6.411 0.000 9 $208,775 $59,982 8 2 4
13697141 Smith, Jane FFS 61 F 7-5 5.78 2.498 12 $64,603 $54,079 8 2 3
49198618 Smith, George W MCO 2 47 M 7-3 6.25 0.000 9 $106,335 $58,474 3 8 12
10070720 Smith, George FFS 62 M 7-5 6.732 0.000 12 $6,326 $62,981 7 3 4
56277059 Smith, Jane S MCO 2 60 F 7-6 6.296 0.000 12 $35,568 $58,901 8 4 8
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 139
The BCI-DSS solution will allow users to run ad hoc queries and do various ―what if‖ analyses to easily
identify members that warrant further investigation. For example, leveraging the underlying stratification, a
user could run a query to identify all members, for a particular population, that has over 6 gaps in care with a
disease progression greater than 2. The results of this type of query will provide a care manager with a
wealth of information on the population and provide a queue of individuals to analyze.
Utilization Analysis
Figure 10-21. Member Utilization Analysis.
The Utilization Analysis provides an understanding of the resources and services consumed by the members
in the Medicaid population. Figure 10-21 shows the number of IP admissions, IP days, emergency room (ER)
visits, office visits, and laboratory, radiology, and therapy units consumed during the last 12 months. This will
again help identify areas and members that may require further analysis and potential investigation as to the
root cause of the identified impact(s). For example, using Jane Smith again, she has 5 short stay IP
admissions (6 days total), 2 of them where preventable and 1 was a re-admission, according to the quality
analysis in Figure 10-20. This means that over 50 percent of her admissions where preventable, counting the
re-admit as preventable. Jane has seen her primary care physician (PCP) in the office 15 times but also went
to the ER 4 times. She has 6 gaps in care and has had no lab tests done, but had 4 radiology services.
The BCI-DSS can be used to paint a very detailed picture of the members’ quality of care, looking at the
potential impact of delivery of care and the utilization of services. These analyses will provide the business
and clinical actionable information for care and program managers to conduct detailed population
management. The goal is to minimize the impacts identified above and improve the outcomes of services
and programs to improve the quality of care, the quality of life, and the care delivery.
Sample Financial and Pharmacy Analyses are shown in Appendix A, Sample Reports.
Population Management Analysis
Member ID Name P
rod
uc
t
Ag
e
Sex
CR
G
S/S
DB
MM
Co
st
(PM
PY
)
Pro
j C
os
t (P
MP
Y)
IP A
dm
IP D
ays
ER
Vis
its
Off
ice
Vis
its
OP
-La
b
Un
its
OP
-Rad
Un
its
OP
-Th
er
Un
its
17500663 Smith, George L FFS 62 M 7-4 5.319 12 $41,056 $49,765 7 36 9 4 25 5 14
14206214 Smith, Jane FFS 55 F 8-5 6.793 3 $62,781 $0 18 58 9 3 92 21 0
21579620 Smith, George L FFS 48 M 7-5 5.559 12 $64,952 $52,006 8 20 67 1 180 49 1
16000599 Smith, George C FFS 68 M 9-6 8.694 12 $20,193 $81,342 9 29 4 2 0 3 0
04882818 Smith, Jane J FFS 48 F 9-4 7.016 12 $160,815 $65,641 10 76 7 0 113 16 0
12844850 Smith, Jane MCO 2 63 F 7-4 5.281 12 $15,467 $49,406 6 11 11 3 0 1 0
24004517 Smith, Jane FFS 47 F 7-4 6.016 12 $23,892 $56,282 5 16 5 0 33 5 0
03936377 Smith, Jane R MCO 2 68 F 7-3 4.523 9 $15,547 $42,314 5 6 4 15 0 4 0
61733632 Smith, Jane S MCO 1 4 F 5-1 0.110 12 $750 $0 6 12 6 0 15 1 15
16330566 Smith, George T FFS 64 M 7-5 6.732 12 $25,824 $62,981 6 18 8 0 79 6 3
32006563 Smith, George MCO 1 79 M 9-6 21.135 12 $13,705 $197,734 9 72 15 0 0 0 0
62457113 Smith, George W MCO 1 65 M 7-4 6.411 9 $208,775 $59,982 5 199 1 0 0 0 0
13697141 Smith, Jane FFS 61 F 7-5 5.780 12 $64,603 $54,079 6 37 16 5 50 23 9
49198618 Smith, George W MCO 2 47 M 7-3 6.250 9 $106,335 $58,474 15 51 23 0 112 44 0
10070720 Smith, George FFS 62 M 7-5 6.732 12 $6,326 $62,981 7 40 4 0 0 3 0
56277059 Smith, Jane S MCO 2 60 F 7-6 6.296 12 $35,568 $58,901 10 42 6 0 67 12 1
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 140
Deloitte’s BCI-DSS solution provides an innovative approach to conducting population management. It
leverages the pure clinical stratification of 3M’s CRGs and the severity adjustment to stratify the population
and to identify the appropriate level of programs and interventions required to improve the health status of
the members.
Typical DSS tools identify only high cost members as the cases that require management, i.e., high cost
procedures, etc. Some tools may promote the use of the grouper output to identify the right population, as
does our BCI-DSS solution. The primary difference between the CRGs and other groupers is that other
groupers use financial data (i.e., cost of services) as an input to the grouping process, and therefore the cost
of service variations are now part of the output, the members’ risk scores. In CRGs, the cost component is
excluded in the grouping of the member into the right status and severity, therefore making the health status
the sole driver. Cost variations are introduced post CRGs grouping.
The BCI-DSS solution approach is proactive in that it also focuses on the cause of the high cost, i.e., disease
progression and quality and utilization of service delivery. Deloitte will assist BMS’ care management
operations by providing the analyses to identify the members that will benefit the most from program and
interventions, touching the entire population through population management.
One goal of proactive identification is to identify those individuals early on in their disease state and hold
back disease progression. This will enhance the individual’s quality of life through improving the quality of
care and delivery of services, which can reduce expenditures.
To accomplish this, we leverage the clinical data model where each member is placed into a mutually
exclusive health status and severity. This data model forms the basis from which BMS can focus its efforts
on the right group of individuals that will benefit the most from care management, i.e., education programs,
disease management, case management, etc.
Through the understanding of each individual’s health status, various clinical and business analyses and
studies can be conducted to provide the picture as to the state of the population – the population’s disease
burden, disease progression, and resource consumption outliers.
The Population’s Health Status and Severity Distribution, shown in Figure 10-22, illustrate the disease status
and severity distribution. Through our experience, we know that the members who fall into the higher status
categories (e.g., 8 and 9) and higher severities in middle status categories (e.g., 5, 6 and 7), illustrated in the
red shaded cells, are the members that have progressed within their disease(s) and are having
complications that drive resource consumption (e.g., inpatient admissions, high utilization of services, etc.),
and these members usually fall into utilization management.
The appropriate members that will benefit the most from program intervention, i.e., disease or case
management, fall into the middle disease statuses and lower severity levels (e.g., 3-1/2, 4-1/2/3/4, 5-1/2/3, 6-
1/2, and 7/1), illustrated in the green and yellow shaded cells in Figure 10-22. The goal is to manage and
educate the member to hold back his /her progression into higher severities and/or movement into high
statuses.
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 141
Figure 10-22. Population’s Health Status and Severity Distribution.
The BCI-DSS applies analytical techniques can further refine the member identification process to target the
most appropriate individuals for program intervention. For example, we know that Diabetes is a disease that
is placing a large burden on the state, from Figure 10-19. The BCI-DSS tool filters could focus just on that
particular disease, shown in Figure 10-23. The BCI-DSS tools can even break that down by what
subpopulation is affected the most, i.e., FFS, MCOs, etc., enabling BMS with the information that could drive
decision making.
Figure 10-23. Population’s Health Status and Severity Distribution for Diabetics.
Analyzing Figure 10-23 shows that 46 percent of the Diabetic population falls into the lower severity levels
and early onset of their disease and may be ideal candidates for either some level of Diabetes disease
management or for participation in an educational program to get them on track to manage their disease.
In addition to the above query and report types, the BCI-DSS tool includes the COGNOS’ SPSS analytical
modeling module. This will enable care managers and medical management users with the ability to build out
predictive analyses, where they can program triggers, based on historical progression trends, to continuously
scan the member’s data for various series of events that could potentially lead to health status progression
and service impacts. The BCI-DSS tool would alert users when these events are identified.
Health Status /Severity 1 2 3 4 5 6 Total
1 – Healthy 649,248 649,248
2 – Significant Acute 91,192 91,192
3 – Single Minor Chronic 54,886 5,696 60,582
4 – Multiple Minor Chronic 8,204 1,969 3,428 1,000 14,601
5 – Single Significant Chronic 153,939 23,415 10,185 2,989 1,329 708 192,565
6 – Two Significant Chronic 64,960 38,546 23,432 19,124 19,124 1,930 157,652
7 – Multiple Significant Chronic 7,236 8,197 15,966 5,101 5,101 1,491 42,680
8 – Complex Malignancies 424 1,845 2,774 3,234 3,234 9,430
9 – Catastrophic 835 2,738 2,314 2,045 2,045 1,553 10,943
WV_DW_DSS-005_3
Health Status /Severity 1 2 3 4 5 6 Total
1 – Healthy
2 – Significant Acute
3 – Single Minor Chronic
4 – Multiple Minor Chronic
5 – Single Significant Chronic 7,124 1,877 708 7 386 10,102
6 – Two Significant Chronic 11,333 6,500 5,250 3,717 3,717 573 33,271
7 – Multiple Significant Chronic 4,488 4,926 8,991 3,219 3,272 1,135 26,031
8 – Complex Malignancies
9 – Catastrophic
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 142
The power of using the BCI-DSS’s clinical and business data analyses, along with the various ways to filter
and drilldown the queries, will provide the most intelligent information to the care and medical managers and
program decision makers. All queries and drilldowns would lead to the lowest level of detail, the individual
member’s PHR.
Care Management Program Performance Monitoring
Supporting care management program activities, BMS’ internal and external outsourced programs, the BCI-
DSS tool can monitor performance. The data warehouse can capture the program enrollment information,
which will become a filter in any of the member analyses. This will enable BMS care managers and program
managers to monitor the performance of the programs to hard outcomes. For example, if BMS contracts with
an MCO to develop a disease management program, BMS’ program oversight could have the ability to
monitor performance to outcomes. These outcomes could include, but not limited to quality of care (i.e., gaps
in care, preventable events, etc.), utilization of services and delivery-of-care patterns as well as health status
progression. The BCI-DSS tool can also be used to either identify the appropriate members for enrollment
into the various programs or could monitor the efficiency of the program’s selection methods.
The goal of care management programs is to improve the health status of the members and hold back the
progression, which can be monitored over time by benchmarking enrolled members to the overall population
in similar disease categories but not enrolled. This begins to form the bases of various cohort studies.
The information obtained from the BCI-DSS solution can be used to negotiate program contracts, as it could
form the basis to establish outcomes based performance service level agreements (SLAs).
Program Management
Program Management is the heart of the Medicaid enterprises. It establishes the strategic direction, defines
policies, monitors activities and provides oversight of all operations. This business area includes a wide
range of planning, analysis, and decision-making activities and depends heavily on access to timely and
accurate actionable information.
The analytics will focus on service utilization, financial and budget analysis, quality and performance analysis
and monitoring and outcomes analysis that will provide the means to perform benefit plan design, rate
setting, healthcare outcome targets, and cost-management decisions.
The BCI-DSS solution will provide the underlying analytics to deliver intelligent information to the users to
make informed decisions. In addition to the analytics discussed above in the Care Management section, this
section will demonstrate the ability to analyze impacts and monitor performance at the provider/program view
as well as establishing information to guide operations, i.e., rate setting, policies making, etc.
The core program management need of any Medicaid agency is insight into the financial picture—what is
driving cost and use trends? Are we tracking to budget? Are we paying our providers appropriately? What is
the likely result of a change in benefit, eligibility, or payment policy? Many systems will provide lists of claims
for summing costs; the BCI-DSS solution does much more by showing the reasons for these cost trends. It is
designed to return accurate information that is well documented and defendable. The financial reporting
capabilities build on the data foundation stored in the data warehouse, which has (i) been carefully validated
and matched to the source data and (ii) adjusted details are carefully managed so that net pay and services
are accurate and consistent.
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 143
Deloitte has designed the BCI-DSS analytics to view the aggregate overall population view, allowing analysts
and decision makers the ability to drilldown and segregate the information for subpopulations. As described
in the operational management section above, the BCI-DSS analytics presents a view of the overall
population with breakdowns by FFS and MCOs, shown in Figure 10-24.
Figure 10-24. Comparison of Subpopulations to the Total Population.
Figure 10-24 shows the financial impacts by entity, which can then be further analyzed to measure sums,
rates and ratios (i.e., per member, PMPM, per 1000, per admit, etc.). The following analyses demonstrate
the detailed elements (i.e., service utilization, resource consumption and costs) captured at the member
level, which can be used to aggregate at various defined grouping levels. Figure 10-25 shows a sample of
the aggregate financial elements processed from the claims detail.
Figure 10-25. Member Financial Analysis.
While this reports views the costs at an aggregate level, i.e., IP costs, outpatient (OP) costs, Rx costs, etc.
the details under each are captured in the data elements. During the data processing activities, to load data
into the data warehouse, we capture costs by various Type-of-Service as well as Site-of-Service levels,
distinguishing between in-network and out-of-network.
WV_DW_DSS-064
Population Mem Avg MM Avg DB DB ImpactAvg Cost
(PMPM)
Proj Cost
(PMPM)
Total 331,318 7 1.124 112.380 $531 $846
FFS 218,301 8 1.562 102.920 $762 $1,250
MCO 1 113,017 7 0.277 9.46 $565 $1,169
Member ID Name
Pro
du
ct
Ag
e
Se
x
CR
G S
/S
Dis
ea
se
Bu
rde
n
MM
Co
st
(PM
PY
)
Pro
jec
ted
Co
st
(PM
PY
)
IP-T
ota
l C
ost
(PM
PY
)
OP
-To
tal
Co
st
(PM
PY
)
OP
-Su
rg C
os
t
(PM
PY
)
OP
-La
b C
os
t
(PM
PY
)
OP
-Rad
Co
st
(PM
PY
)
OP
-Th
er
Co
st
(PM
PY
)
Rx
Co
st
(PM
PY
)
30494926 Smith, Jane N FFS 15 F 8-4 4.649 10 $405,168 $0 $163,170 $100,983 $0 $3,417 $426 $126 $141,015
62449682 Smith, Jane M FFS 1 F 6-4 3.201 6 $404,099 $29,949 $122,058 $282,041 $0 $75 $0 $0 $0
22184907 Smith, George L FFS 19 M 5-4 5.200 10 $364,781 $123,499 $0 $530 $0 $61 $118 $32 $364,250
62430785 Smith, Jane R FFS 1 F 6-5 3.697 1 $356,486 $34,591 $352,415 $1,910 $0 $25 $842 $0 $2,160
15856365 Smith, George V FFS 24 M 9-4 6.970 9 $356,312 $65,207 $0 $5,992 $0 $0 $2 $0 $350,320
24459233 Smith, George D FFS 18 M 6-3 4.643 12 $329,261 $43,437 $0 $2,484 $0 $61 $41 $57 $326,777
19153618 Smith, George
W MCO 1 39 M 6-1 2.253 12 $321,227 $21,081 $0 $3,060 $0 $276 $1,767 $0 $318,167
28837451 Smith, George D MCO 1 46 M 9-5 9.330 9 $272,334 $87,293 $0 $440 $0 $350 $0 $0 $271,894
61662138 Smith, Jane J FFS 3 F 9-6 21.135 8 $253,600 $197,734 $113,616 $119,963 $359 $100 $153 $350 $20,021
03381952 Smith, George E FFS 46 M 9-6 21.135 12 $248,987 $197,734 $244,764 $4,223 $132 $11 $261 $0 $0
62465735 Smith, George A MCO 1 1 M 5-5 1.119 8 $247,292 $10,467 $246,602 $595 $0 $0 $0 $0 $95
45221942 Smith, George P MCO 1 17 M 6-1 3.467 12 $244,367 $32,438 $0 $237,126 $0 $0 $0 $0 $7,241
27789314 Smith, George P FFS 22 M 9-4 10.139 12 $240,332 $94,855 $166,505 $58,347 $0 $797 $1,723 $0 $15,480
02342989 Smith, Jane L FFS 41 F 6-5 4.182 12 $239,519 $39,122 $227,595 $11,246 $0 $1,497 $972 $0 $678
59705883 Smith, Jane MCO 2 19 F 9-4 10.139 5 $238,431 $94,855 $188,360 $48,685 $0 $125 $1,099 $0 $1,386
03864271 Smith, George D MCO 2 29 M 9-2 8.700 12 $232,680 $81,390 $7,032 $213,998 $0 $193 $1,384 $0 $11,650
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 144
The BCI-DSS financial analytics will support the actuarial analysis and reporting by leveraging the clinical
risk information and actual and predictive cost information. Historical trends in cost, utilization and risk by
group, combined with predictive capabilities, make this a very powerful tool for actuaries. The BCI-DSS
analytics will support risk profiles, including impact on risk and cost using the following data elements: SIC
codes, age/gender, policy age, medical cost trend, member enrollment and diagnosis/severity of illness. The
BCI-DSS solution will provide the ad hoc reporting capabilities to view the information in various dimensions.
The underlying clinical power of using 3M’s CRGS is setting and reviewing payment rates. For payments,
particularly capitation rates, to be equitable they should reflect the clinical needs of the covered population. A
provider who delivers low cost care may not really be a low cost provider if its costs are controlled by
avoiding risk rather than by delivering services efficiently. Indeed, the provider may actually be inefficient and
rely on favorable risk selection rather than clinical oversight to contain costs. With CRGs, BMS can review
provider performance after accounting for risk selection. You can identify efficient providers and you can offer
equitable payments. Detailed provider analytics will be discussed in the following section of this proposal.
Program Integrity Management
Program Integrity Management is a business area that is in its infancy stage. It will continue to mature as
agencies increase access to clinical data to improve the capability to identify program abuse cases.
Currently, this business area primarily focuses on the utilization of Surveillance and Utilization Review (SUR)
activities and program compliance to include auditing and tracking medical service necessity,
appropriateness of care delivery, quality of care, fraud and abuse, and erroneous payments.
The BCI-DSS solution will provide information about individual providers and members and identify different
types of service, cost and quality outliers (both high and low) that will require a more detailed analysis and
drill down into the root causes. These actions will form the basis to develop cases, investigate activities,
establish interventions, report on findings and resolve cases. The BCI-DSS solution will provide the means to
move traditional retrospective SUR activates to a concurrent and prospective means of analysis as our
solution includes the COGNOS SPSS analytical and predictive modeling module.
Deloitte’s BCI-DSS solution approach is a combination of surveillance and utilization review and fraud and
abuse detection. The data warehouse will store the vast amounts of data that can be used to develop
normative service delivery patterns to benchmark current activities to identify and isolate suspicious practice
patterns. Our approach begins with the analysis of providers to identify outliers of both over- and under-
utilization of services, quality of services, service/drug prescription and referral patterns, and billing practices.
The BCI-DSS tool will provide the means to analyze providers for these various service patterns while also
providing the means to drilldown to investigate the outliers in order to establish causes. Use of exception
profiling as a starting point for case development is a viable technique in the detection and control of fraud
and abuse. For example, in our designed provider analytical analyses, program integrity (PI) managers will
have access to easily identify outliers, shown in Figure 10-26.
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 145
Figure 10-26. Provider Utilization Analysis.
In Figure 10-26, it is identified by provider the services and utilization provided to their patient panels. This
can be used to compare service utilization against the aggregate as well as peers. For example, this report
shows that Dr. Robert Johnson has 3 patients in his panel with an average disease burden of 1.977. His
panel had an average of 8 emergency room visits and 1 office visit for the last 12 month period. This would
flag the question why do his patients go so often to the emergency room and why is the number of office
visits so low, when noticing that he has a sicker population than most of his peers. This information would
flag the need to drilldown further and view this provider’s performance and view his patients PHR to identify
further investigative needs.
Figure 10-27, takes a similar approach except that it looks at the financial analysis to identify cost outliers.
Total Providers Panel Avg DB
Avg PI Risk Score
IP Admits
P1kMPY
IP Avg LOS
ER Visits
(PMPY)
Office Visits
(PMPY)
Referrals
(PMPY)
Lab Tests
(PMPY)
Rad Units
(PMPY)
Ther Units
(PMPY)
106 203 0.733 7 162.6 0.4 0.7 0.2 .08 2.9 1.2 1.1
Provider ID Provider Name Panel Avg DB PI
Risk Score
IP Admits
P1kMPY
IP Avg LOS
ER Visits
(PMPY)
Office Visits
(PMPY)
Referrals
(PMPY)
Lab Tests
(PMPY)
Rad Units
(PMPY)
Ther Units
(PMPY)
143303 Hancock, Joan K 7 0.298 8 142.9 0.4 0.9 1.0 2.1 0.9 1.7 0.1
146844 Harris, Rosemary A 6 0.536 6 0.0 0.0 0.0 1.2 0.0 1.8 0.0 7.7
150251 Hancock, Joan P 1 1.602 5 0.0 0.0 0.0 2.0 0.0 0.0 3.0 0.0
153545 Hancock, Joan J 4 1.130 7 0.0 0.0 0.5 0.0 0.0 18.0 14.8 0.0
142446 Johnson, Robert D 1 0.186 4 0.0 0.0 0.0 7.0 0.0 0.0 0.0 0.0
151397 Hancock, Joan L 1 3.586 11 0.0 0.0 3.0 2.0 3.0 6.0 6.0 3.0
153519 Hancock, Joan R 5 0.111 7 0.0 0.0 0.6 0.4 0.0 3.0 1.2 0.2
146668 Johnson, Robert L 1 0.077 12 0.0 0.0 1.0 4.0 2.0 9.0 0.0 0.0
140333 Hancock, Joan R 1 0.236 10 0.0 0.0 3.0 1.0 0.0 4.0 1.0 0.0
146318 Johnson, Robert J 1 1.603 13 0.0 0.0 1.0 3.0 0.0 3.0 1.0 0.0
146603 Hancock, Joan L 6 4.536 18 166.7 1.0 0.8 3.0 2.0 0.7 1.3 0.0
152220 Johnson, Robert C 7 0.105 3 428.6 2.0 0.9 0.0 0.0 1.3 0.4 0.0
145671 Hancock, Joan L 9 0.464 4 111.1 1.1 2.4 1.1 1.0 1.6 0.6 0.9
146910 Harris, Rosemary S 3 0.801 7 100.0 1.3 0.7 0.0 1.0 6.0 2.7 0.0
150310 Johnson, Robert H 1 0.841 6 0.0 0.0 2.0 0.0 0.0 7.0 4.0 0.0
143792 Johnson, Robert P 3 1.977 12 333.3 0.3 8.0 1.0 0.3 2.7 3.7 0.0
146378 Johnson, Robert M 1 0.920 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
142647 Hancock, Joan H 5 0.276 3 0.0 0.0 0.2 0.8 1.0 1.4 0.2 0.0
146216 Johnson, Robert K 4 1.258 12 0.0 0.0 1.3 0.0 0.3 1.3 2.5 0.0
153674 Harris, Rosemary J 6 0.378 10 166.7 0.8 1.0 0.0 1.2 4.0 2.2 0.0
145062 Johnson, Robert E 3 1.174 9 0.0 0.0 0.7 0.0 0.0 3.7 0.0 0.0
151482 Johnson, Robert M 1 0.236 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 146
Figure 10-28. Provider Financial Analysis.
In Figure 10-28, it shows the total cost of delivering care by provider to the patient panels. In this analysis it
shows that overall average disease burden is .733 with an average total cost of $2,314 PMPY. When
comparing the detailed list of providers, it is easy to see the outliers that may require further investigation
regarding the reason that their costs are so high. Users can view the disease burden, which is a case mix
and severity adjusted index, to compare performance patterns. For example, Dr. Rosemary Harris has 6
patients in her panel with an average disease burden of .536, healthier than the population average of .733.
Her cost for care delivery is almost 7.5 times higher than average (i.e., aggregate benchmark). This would
raise the question why her costs were so high compared to her peers when her patients are not sicker.
Further investigation into the reasoning could be accomplished by drilling down into her patients’ PHR to
determine the cause.
The detailed analysis required to focus efforts in program integrity is to be innovative in the development of
various reports. While PI Programs look at outliers, PI managers must be able to not only identify high
outliers but also the low, the underutilization of services that are affecting the quality of care of the members.
For example, the implementation and expansion of MCOs in state health care programs have forced
changes in the performance of utilization control and program integrity. The financial incentive for abuse of
the Medicaid program in FFS is for overutilization of services to increase payments; whereas in MCO, it
changes to underutilization of service. The financial incentive for the MCOs or individual providers who are
primary care case managers is to provide less service since their payments are not based on the number of
services provided. This has also changed the focus of utilization review since service providers are now
motivated to withhold services (and thus reduce costs) rather than to over use them as in traditional FFS.
Total Providers Panel Avg DB
Total Cost (PMPY)
IP Cost (PMPY)
OP - Total Cost
(PMPY)
OP - RX Cost
(PMPY)
OP - Lab Cost
(PMPY)
OP - Rad Cost
(PMPY)
OP - Ther Cost
(PMPY)
106 203 0.733 $2,314 $0 $1,761 $553 $22 $93 $45
Provider ID Provider Name Panel Avg DB Total Cost
(PMPY) IP Cost (PMPY)
OP - Total Cost
(PMPY)
OP - RX Cost
(PMPY)
OP - Lab Cost
(PMPY)
OP - Rad Cost
(PMPY)
OP - Ther Cost
(PMPY)
142446 Johnson, Robert D 1 0.186 $209 $0 $0 $209 $0 $0 $0
143303 Hancock, Joan K 7 0.298 $172 $0 $154 $17 $4 $36 $6
146668 Johnson, Robert L 1 0.077 $0 $0 $0 $0 $0 $0 $0
146844 Harris, Rosemary A 6 0.536 $17,320 $0 $16,612 $708 $20 $0 $381
150251 Hancock, Joan P 1 1.602 $4,127 $0 $158 $3,969 $0 $130 $0
151397 Hancock, Joan L 1 3.586 $22,571 $0 $17,522 $5,049 $67 $1,244 $191
153519 Hancock, Joan R 5 0.111 $662 $0 $620 $42 $10 $495 $0
153545 Hancock, Joan J 4 1.13 $14,767 $0 $14,569 $198 $113 $1,392 $0
140333 Hancock, Joan R 1 0.236 $99 $0 $99 $0 $74 $0 $0
143792 Johnson, Robert P 3 1.977 $2,991 $0 $1,473 $1,519 $0 $118 $0
145671 Hancock, Joan L 9 0.464 $2,009 $0 $1,319 $690 $18 $119 $67
146318 Johnson, Robert J 1 1.603 $3,523 $0 $597 $2,927 $30 $114 $0
146378 Johnson, Robert M 1 0.92 $898 $0 $285 $614 $0 $0 $0
146603 Hancock, Joan L 6 0.536 $436 $0 $417 $19 $7 $208 $0
146910 Harris, Rosemary S 3 0.801 $5,102 $0 $4,979 $123 $133 $82 $0
150310 Johnson, Robert H 1 0.841 $2,745 $0 $1,854 $891 $54 $846 $0
152220 Johnson, Robert C 7 0.105 $31 $0 $31 $0 $10 $0 $0
141432 Johnson, Robert A 1 2.386 $8,508 $0 $6,776 $1,732 $39 $0 $0
141808 Johnson, Robert R 1 0.106 $0 $0 $0 $0 $0 $0 $0
142647 Hancock, Joan H 5 0.276 $1,656 $0 $1,548 $108 $14 $0 $0
145062 Johnson, Robert E 3 1.174 $3,231 $0 $540 $2,691 $41 $0 $0
145546 Johnson, Robert A 3 0.083 $3 $0 $0 $3 $0 $0 $0
State of West Virginia DW/DSS Bureau for Medical Services RFP MED11015
Solution Alignment With BMS’ Business Needs Section 10.0 Page 147
During the business requirements gathering phase of the DW/DSS implementation, we will work with the
Program Integrity program and identify the requirements to develop innovative approaches to query the data
and generate the analytics to easily identify outliers. We will create the analytics to identify variation in
delivery patterns and billing activities and program them into the BCI-DSS SPSS module. This will leverage
the power of the designed solution to actively and continuously identify outliers.