value based care analytics - florida chapter of hfma€¦ · value based care analytics an overview...

Value Based Care AnalyticsAn Overview of Measures, Metrics, and Enabling TechnologiesMay 2019

Copyright © 2019 Deloitte Development LLC. All rights reserved. 2

BiosThe road to action from data begins with data collection and relies on the integration of analytics into management review and staff workflows

Pam PriceSpecialist LeaderAtlanta, GA

Pam has more than 25 years of experience spanning Health Plans, Provider groups, and pharmaceuticals. Pam’s expertise includes risk adjustment, product development, strategic planning and execution, financial leadership and mergers and acquisitions

Brendan NolanSenior ConsultantNew York, NY

Brendan has over 9 years of experience working with both plans and providers to integrate operations surrounding value based care. His focus is on creating common analytics that allow collaborative programs

Angie DiazManaging DirectorKansas City, MO

Angie is a managing director in the health care provider sector with over 20 years of experience in the industry. Her primary focus is revenue cycle process improvement and revenue management operational re-design


Section Topic

1 Background/Context

2 Common Analytics & Dashboard Samples

3 Advanced Technologies

4 Analytics Roadmap

Agenda

Background & ContextPerspective on the future of Value Based Care


1. US Census Bureau https://www.census.gov/newsroom/stories/2019/older-americans.html2. CMS: https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nationalhealthaccountshistorical.html

Unsustainable Healthcare Spend

2026 US spend:

(projected)

$5.7 trillion

19.7% of GDP2

2016 US spend:

$3.3 trillion

17.9% of GDP2

Healthcare Market ForcesPopulation aging coupled with healthcare spending trends have been shifting health plan and provider focus to value over volume since the 2010 Affordable Care Act

Value Focused Legislation

2010’s Affordable Care Act remains in

place and is pushing health plans and

providers towards value based

reimbursement

Aging US

Demographic

2023 US population

> 65 (projected):

62 Million1

2019 US population

> 65:

48 Million1

https://www.census.gov/newsroom/stories/2019/older-americans.html

https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nationalhealthaccountshistorical.html


Varied or misaligned

incentives by

stakeholder

Lack of consensus

on standards & priorities

Uncontrollable factors (e.g.

patient adherence to

therapy)

Uncertain regulatory

environment expectations

Challenges in

collecting and

analyzing necessary

data

Lack of enabling

systems to

administer program

The Conundrum: Current barriers are slowing widespread adoptionCreating successful value based programs is one of the biggest challenges in health care. There are many challenges that must be addressed in order to effectively drive adoption of value based care programs


Market Forces are Pushing Providers Towards Accepting RiskBut each organization is moving at their own pace as they: a) evaluate their ability managing more risk, and b) develop the necessary tools and skills needed

Fee for Service Full CapitationBundled Payments

Upside/Downside Contracts

Upside Only Contracts

As provider organizations move towards accepting incremental risk, many will increasingly

rely on clear insights from analytics to guide their operations

Low Risk High Risk

Leaders need insight on performance against ‘bonus’ metrics that exist in upside only contracts (e.g. quality measures, annual wellness visit completion)

Leaders need a clear picture of their patient panel’s risk and how their

organization is managing the overall costs of that population

Leaders need visibility into how costs are managed by episode of care, by risk stratification, and

by chronic condition

Leaders need to have a complete understanding of how to manage costs and practice patterns within their provider network, and begin to have analytics proactively suggest

areas for tighter focus/control


Value Based Care Analytics – Not One-Size-Fit-AllEach health system might approach acquiring needed analytics differently based on size and organizational structure

Large Single EMR Health System ACO/ MSO/ MCO IPA Small Practice

Traits:• Well resourced• Single EMR system• Aligned goals/operations• Low ratio of analytics overhead cost/

provider

Traits:• Well resourced• Potential single EMR vendor• Multiple larger EMR instances• Potentially competing

goals/operations• Medium ratio of analytics overhead

cost/provider

Traits:• Resourcing varies• Potentially aligned EMR vendors• Multiple smaller EMR instances• Varied access to data• Potentially competing

goals/operations• High ratio of analytics overhead

cost/provider

Traits:• Resource constrained• Smaller EMR instance• Varied access to data• Aligned goals/operations• Medium-High ratio of analytics

overhead cost/provider

Typical Method for Analytics:• Build out reporting data warehouse• Evaluate purchasing functionality

from EMR vendor• Advocate for raw data from health

plan

Typical Method for Analytics:• Build out reporting data warehouse

using custom data transformations• Evaluate EMR consolidation

incentives• Advocate for raw data from health

plan

Typical Method for Analytics:• Build out reporting data warehouse

using data exchange standards• Evaluate EMR consolidation

incentives• Advocate for raw data from health

plan

Typical Method for Analytics:• Advocate for raw data from health

plan• Advocate for packaged reports from

Health Plans• Determine what features may be

available through EMR vendor

Key Take-Away• What to build

Key Take-away:• What to build

Key Take-away:• What to build

Key Take-away:• What to request from health plan

and EMR vendor

AnalyticsThe Connective Tissue Between Data and Efficient Decision Making


Dashboard DomainsValue based care analytics typically align with the foundations of risk bearing contracts. Metrics included in each dashboardwill vary based on the population managed and the specifics of your contracts.

Medical Cost Management

• Utilization by Service Category

• Utilization by Site of Service

• Cost by Risk Stratification

• Cost by Chronic Condition

• Cost by Episode of Care

• Network Referral Patterns

• Admission/Readmission Patterns

• Generic Rx Rate

Risk Adjustment

• Annual Wellness Visit Completion

• Demographics, Status, Panel Shifts

• Predictive Analytics

• Chart Review/Coding Quality

• Financial Projections

• Integration at the point of care

Quality Measures

• Overall Stars Performance

• Gap Closure Lists (By Measure)

• Gap Closure Lists (By Patient)

• “Propensity to Act”

• Integration at the point of care

Measures of provider or patient behavior tied to clinical quality, safety, or contractual incentives

Measures of the illness burden of your population that should ultimately be reflected in any capitated payments

Measures of utilization, price, and network referral patterns that drive medical spend


Example - Quality Dashboard #1 – Overall Measure Dashboard

Reports as of:09/30/2018

QualityGlobal Filters

Chronic Conditions

(All)

Ethnicities & Races

(All)

PPPM

(All)

Risk Score

(All)

Attribution Type

(All)

Benchmark

(All)

Year

Year Type (Calendar/Trailing)

(All)

Genders

(All)

Languages

(All)

Age Ranges

MA

Payers

(All)

Member Products

(All)

Provider

(All)

Overall Performance

(All)

79%

Overall Performance

Cancer Screening

Disease Prevention

Disease Management

Annual Breast Cancer Screen

Colorectal Cancer Screening

Reducing Risk of Falling

Annual Flu Vaccine

Monitoring Physical Activity

Adult BMI Assessment

Diabetes Care – Eye Exam

Diabetes Care – Kidneys…

Diabetes Care – Blood Sugar

Controlling Blood Pressure

MeasureDomain NumPerformance Denom # to Target

%

24,231

42,344

16,734

59,012

59,012

78,023

12,986

12,986

12,986

16,054

20,596

34,298

12,048

25,375

44,259

67,880

9,350

8,830

12,077

13,485

85%

81%

72%

43%

75%

87%

72%

68%

93%

84%

485

3,811

3,849

23,605

1,770

0

3,376

2,727

0

803

Compared to Calendar Year

74% at this point in time

last year

87%Compared to Trailing Year 83% at this point in time last year

4.5/5

4.0/5

CY

PY

Display “Trailing Year” to reflect how well Providers current behaviors would be scored

Display “Calendar Year” to reflect how much work remains to achieve the desired outcome

For most dashboards, allow the fields to be displayed to be configurable. For quality metrics, consider including thresholds, prior year scores, exclusions, % to target.

Create click-throughs that drill down to the non-compliant patients for an individual measure or across all measures

1 2

3

4

1

2

3

4


Example - Quality Dashboard #2 – Patient/Measure Level Detail

“Propensity to Act” algorithms allow users to create outreach programs tailored towards patients based on their likelihood to engage and take action

Patient lists should include all relevant information for outreach. Strong patient lists will include preferred contact methods and outreach history information.

Patient list should enable a user to see all gaps for a patient. One outreach should be done to address and discuss all gaps.


QualityGlobal Filters

Chronic Conditions

(All)

Ethnicities & Races

(All)

PPPM

(All)

Risk Score

(All)

Attribution Type

(All)

Benchmark

(All)

Year

Year Type (Calendar/Trailing)

(All)

Genders

(All)

Languages

(All)

Age Ranges

MA

Payers

(All)

Member Products

(All)

Provider

(All)

(All)

1

2

3

1

2 3

Care System D

Care System D

Care System D

Practice A


Risk AdjustmentIn the Value Based Care world – Risk Adjustment Analytics most strongly correlate with analytics on your organization’s top line revenue (leaving cost and quality bonus measurements aside)

Domain Categories

Demographics, Status, and Patient Panel Shifts – General demographic information and population shifts (separates signal from noise)

Predictive Analytics – Identification of potential HCC Conditions in the population needing validation, results in adds and deletes

Financial Forecasting – Estimation and projection of expected revenue based on population risk

Chart Review/Coding Quality/Productivity – Productivity and efficiency metrics for coding staff

Initiative Reporting – Reporting of operational initiatives for risk capture (e.g. Wellness visits)

Compliance – Turnaround time and chart reviews

Note: While there are many topics that a report consumer may want to know about that are not readily apparent in the domain categories, those topics can be addressed through further filtering the data

Patient Encounters – For the encounter year, how well is the health system able to hold wellness visits with their population


Example - Risk Adjustment Dashboard #1 – Patient Encounters

A health system may have focused operational initiatives aimed at engaging patients for their Annual Wellness Visits and/or In-Home Assessments.

Dashboards should be used to measure the success and/or failure points of the engagement initiatives

An entry level Patient Encounter dashboard will track Annual Wellness visit completions

More advanced dashboards will rely on developing predictive analytics that will suggest areas where conditions are likely to be incomplete or inaccurate

20

,00

01

0,0

00

Fully Documented

HCC

HCC Documented Still Predicted

Had Encounter but no HCC

Interaction but no

EncounterNo Interaction

80,000 (+0.9%) 55,000 (+1.0%) 65,000 (+1.2%) Population 60,000 (+1.4%) 40,000 (+1.3%)

26.7% (-.01%) 18.3% (-.02%) 21.7% (+0%) % Population 20.0% (+.01%) 13.3% (+.01%)

N/A 3% (-1.1%) 4% (-0.9%)Non Recaptured HCC Distribution

5% (-0.8%) 3% (+1.3%)

Patients w/ Encounter

+1.2% (YOY)

67%200,000 patients

Refresh Rate

+1.2% (YOY)

85%

Risk Adjusting Encounters as of 05/01/2019

Patient Encounter Programs

Need for Encounters

% Reached% Scheduled Appointment

% with Encounter

% Reached% Scheduled

Home Assessment

% with Encounter

80%16,000 patients

65%6,500 patients

60%12,000 patients

30%3,000 patients

25%2,500 patients

50%10,000 patients

75% of reached

46% of reached

83% of scheduled

83% of scheduledJ F M A M J J A S O N D

Pat

ien

ts w

/ En

cou

nte

rs

Month

20k

10k

15k

5k

25kAnnual Wellness Goal

Annual Wellness Actual In-Home Assessment Actual

In-Home Assessment Goal

IHA

Tar

gete

d

Pat

ien

tsA

WV

Tar

gete

d

Pat

ien

ts

12

,50

0 (

41

.7%

)

Tota

l P

atie

nts

wit

h E

nco

un

ter

( %

Tar

gete

d)

NOTE: Parentheses = YOY change in rate


Risk Adjustment Dashboard

Global Filters

Demographics:

Stayers/Leavers/Joiners

(All)

Age

(All)

Patient Cohort

(All)

Gender

(All)

MA Risk Model

(All)

Diagnoses:HCC

(All)

Benchmark

(All)

Organizational:Region/Medical Center/Provider

(All)

Internal/External

(All)

Population/Contract:

State County Code (SCC)

(All)

Product

(All)

MA/ ACA

(All)

Attribution Method

(All)

Provider Specialty

(All)

Export Patent Detail

1

2

3

4

1

2

3

4


Example - Risk Adjustment Dashboard #2 – Condition Prevalence

0%

5%

10%

15%

20%

25%

0% 5% 10% 15% 20% 25%

HCC HCC DescriptionNational

Benchmark HCC Prevalence Patients

Variance to

National Rate

107 Vascular Disease With Complications 1.82% 1.44% 375 -20.88%

108 Vascular Disease 17.99% 7.90% 2053 -56.09%

017 Diabetes With Acute Complications 0.36% 0.28% 74 -22.22%

018 Diabetes With Chronic Complications 20.24% 12.18% 3166 -39.82%

019 Diabetes Without Complications 9.11% 6.74% 1752 -26.02%

122 Prolif Diabetic Retinopathy & Vitreous Hemorrhage 0.90% 0.41% 107 -54.44%

021 Protein-Calorie Malnutrition 1.80% 0.77% 199 -57.22%

085 Congestive Heart Failure 12.87% 7.75% 2014 -39.78%

096 Specified Heart Arrhythmias 12.91% 10.63% 2763 -17.66%

111 COPD 15.89% 8.84% 2297 -44.37%

134 Dialysis Status 0.22% 0.08% 22 -63.64%

135 Acute Renal Failure 3.99% 2.37% 615 -40.60%

136 CKD, Stage 5 0.23% 0.07% 19 -69.57%

137 CKD, Severe (Stage 4) 0.89% 0.55% 144 -38.20%

058 Major Depressive, Bipolar, and Paranoid Disorder 11.52% 8.94% 2325 -22.40%

Metric Description Value

Current Yr. Avg. HCCs/Mem. 1.45

Prior Year HCCs/Patient 1.50

Present YOY % Difference -3.44%

Forecast HCCs/Patient 1.55

Forecast YOY % Difference +3.33%

Total Predicted HCCs 50,834

Avg. Conversion Rate 9%

Projected Captured HCCs 4,576

Condition Prevalence v. Benchmark

Benchmark Prevalence

Act

ual

Pre

vale

nce

Condition Capture Rate

Export High Variance Patients

Significant VarianceActual = Benchmark(Size) = # of Patients

Note: Codes from seasonal conditions (e.g. pneumonia) are excluded from the graph and chart

HCC: 111Description: COPD

Patients: 2297


Risk Adjustment Dashboard

Global Filters

Demographics:

Stayers/Leavers/Joiners

(All)

Age

(All)

Patient Cohort

(All)

Gender

(All)

MA Risk Model

(All)

Diagnoses:HCC

(All)

Benchmark

(All)

Organizational:Region/Medical Center/Provider

(All)

Internal/External

(All)

Population/Contract:

State County Code (SCC)

(All)

Product

(All)

MA/ ACA

MA

Attribution Method

(All)

Provider Specialty

(All)

Any “System Level” analytics should have the capability to drill down via filters or export raw details. Exports should be used to allow customers to take action on the missed or inaccurate conditions.

Advanced practices will find a way to incorporate these actions into the EMR, so the actions are present at the point of care.

Any predictive analytics should be balanced to look for missing, as well as potential coding errors. Analytics looking only for missing codes limit the opportunity to identify anomalies or trends in the data that may result in compliance risk.

1

2

1

2


Investigation of Medical Cost Management Opportunities begins with viewing cost trends on aggregate and performing deeper dives to understand specific drivers

Medical Cost Management Analytics

Evaluate utilization of services or service categories against benchmark e.g.Advanced Imaging, ED Days

Utilization by Service Category

STL Best-In

Class

Before After

Provide the data to identify the appropriate level of care within the cost appropriate setting

Utilization by Site of Service

$$$

$$

$

Cost by Condition

Evaluate costs and utilization to manage chronic conditions against regional benchmarks

Types of Opportunities Typically Identified

Cost by Risk Stratification

Evaluate how effectively your providers are managing their highest risk, rising risk, and healthy populations

Network Leakage

Evaluate referral patterns, and procedures that commonly occur out of network

Cost Per Episode of Care

Cost Per Episode of care (commonly bundled services) e.g. Comprehensive Joint Replacement, Cardiac Rehabilitation

IP/ED/SNF Admission Management

Track Admissions, Re-admissions, preventable re-admissions, Average length of stay and discharge to locations for inpatient settings of care

Generic Rx Rate

Monitor Generic Prescribing rates for providers to identify areas where lower cost alternatives are available


Example - Medical Cost Management (MCM) Dashboard


MCMGlobal Filters

Chronic Conditions

(All)

Ethnicities & Races

(All)

PPPM

(All)

Risk Score

(All)

In/Out of Network

(All)

Benchmark

(All)

Date Range

PCPs

(All)

Genders

(All)

Languages

(All)

Age Ranges

MA

Payers

(All)

Member Products

(All)

DRGs

(All)

Service Provider

(All)

POS

Hospital Outpatient

Primary Dx

(All)

Primary Procedure

(All)

MM/DD/YYMM/DD/YY

A robust set of filters enables real-time free-form investigation of cost drivers and allows for flexible investigation

Benchmarks will vary depending on what data is available:

• Purchased benchmarks

• Historical data

• Plan provided data

Utilization per 1000

Utilization per 1000

J F M A M J J A S O N DCurr

ent

Year

Prior

Year

Hospital Outpatient

Chemotherapy

Home Health

Observation Days

Occupational Therapy

Outpatient - Dialysis

Outpatient - ER

Outpatient – Mental Health

Outpatient – Subs Abuse

Outpatient Surgery

Physical Therapy

Speech Therapy

Service Category (Minor)Service

Category

(Major)

Health System

Utilization

Benchmark

Utilization

% DiffCurrent Annualized

Expenses

Savings

Opportunity

An Effective Medical Cost Management Dashboard should answer:

- How are we doing managing costs?

- Where are the opportunities?

- What is the quantified opportunity?

- How can we take action?

Where the opportunities exist may involve exploring filters and views – but each view should make the answers to the other questions apparent

50.0

2.6

3.7

93.4

161.9

53.1

22.0

143.1

230.3

17.2

31.1

7.3

5.4

37.2

165.7

46.1

11.1

125.2

157.1

11.3

37.8%

N/A

N/A

60.2%

N/A

13.2%

49.5%

17.9%

31.8%

34.3%

$23M

$54M

$12M

$65M

$120M

$8M

$10M

$36M

$43M

$12M

$8.7M

N/A

N/A

$39.13M

N/A

$1.1M

$4.95M

$6.4M

$13.7M

$4.1M

18.7 15.0 19.8% $56M $11.1M

1

1

2

2

Advanced TechnologiesAnd Their Corresponding Jargon


Predictive Analytics are used throughout the Healthcare industry to estimate the likelihood of future outcomes based upon historical data or related data

Risk Scoring for chronic

diseases and population

health

Avoiding 30-day Hospital

Readmissions

Averting Patient No-

shows

Predicting Patient

Utilization Patterns

Managing the Supply Chain

Ensuring Strong Data

Security

Developing Precision Medicine and New Therapies

Improving Patient

Engagement and

Satisfaction

Identify high risk and rising risk patients to help avoid developing long-term health conditions that are costly and complex to treat

Use behavioral patterns to create meaningful care plans, predict a patient’s willingness to participate, and prioritize those patients who are likely to engage in healthy behaviors to reduce the risks of chronic diseases

Supplement traditional clinical trials and drug discovery techniques using analytics. Providers are able to choose therapies with high success of likelihood by matching patient genetic information with previous patients’ results.

Identify patients when risk factors occur during a hospital stay (infection development, extended length of stay), indicating a high likelihood of readmission within the 30-day window.

Identify patients likely to skip appointments without advance notice.

Providers can send additional reminders to at-risk patients, offer transportation or suggest rescheduling and offer open slots

to other patients with acute needs.

Source: 10 High-Value Use Cases for Predictive Analytics in Healthcare, HealthITAnalytics newsletter

Monitor patterns in data access, sharing and utilization to create early

warnings when unexpected activity occurs

Support decisions around negotiating pricing, reducing supply variation and

optimizing the order process

Anticipate utilization patterns to enable optimal staffing levels, help reduce wait

times and raise patient satisfaction. Visualization tools can be used to model

patient flow patterns and highlight opportunities to make workflow

adjustments or scheduling changes.

Practice

Management

Medical

Management


Robotic Process Automation & Cognitive Automation

Many healthcare organizations are starting to jointly apply robotic process automation and cognitive automation to a broad spectrum of processes, including Utilization Management (UM)

Deterministic Outcomes Probabilistic Outcomes

Realm of Robotic Process Automation (RPA) Realm of Cognitive Automation (C.A.)

“Mimics Human Actions”

Robotics Process Automation is the process of automating simple defined tasks (think ‘Macros’)

RPA Realm:• Rules-based tasks• Operational processes

“Augments Human Intelligence”

Cognitive Automation is the process of capturing a significant volume of decisions made on a set of criteria, and using that data to construct/train a model that can probabilistically predict the outcomes of future decisions on similar criteria

Cognitive Realm:• Cognitive analytics• Decision making

Robotics Process Automation and Cognitive Automation, in brief:

Emerging health system core operations use cases include:

Prior Authorizations: Augment prior-auth review using advanced analytics to further automate clinical decision making (vs. manual review) and triage cases to appropriate staff level

Appeals and Grievances: Triage appeals and grievance cases to appropriate staff level for review and/or decisioning

Predicting Health Conditions: Measure correlation between related datasets (e.g. Prescriptions and vitals) to confirmed health conditions (on the problem list)


Optical Character Recognition (OCR) and Natural Language Processing (NLP)

OCR and NLP are commonly used jointly to ‘digitize’ paper records (fax or EMR Notes) and parse them for key insights

Optical Character

Recognition

Natural Language Processing

Ocular Character Recognition (OCR) can be used to convert Faxes or PDFs into discrete data elements that can be used by machines

Natural Language Processing (NLP) logic is used to convert ‘free text’ into complex relationships based on key words found in the free text

Applications

Convert fax or paper mail sent in for a prior-authorization request into a digital format that can automatically be filed and stored with other meta data (sender, date of receipt)

Transform paper electronic medical charts into digital text that can be searched and reviewed using automated processes

Comb through text in prior-auth requests to pull out data elements for patient, provider, diagnoses, and requested procedure. Once the data elements have been accessed, rules can be created to triage the request. Combined with OCR, fax or mail requests can be automated.

Comb through the free text in electronic medical records to identify potential candidates for HCC codes that have been missed or miscoded to ensure more accurate representation of risk

Parse and Triage Customer Service Requests

Getting Started


Analytics FrameworkThe road to action from data begins with data collection and relies on the integration of analytics into management review and staff workflows

Data Collection Analytics Operations

Advocate for Raw Data

Request data from ecosystem partners to be used in analytics:

1) Claims data from your payers

2) Lab feeds from lab partners

3) ADT feeds from inpatient partners

4) Cost data

5) Referral network data

Create a Data Lake

Store data in its native, raw format so that it is unconstrained in future uses

Integrate and Harmonize

(Data Warehouse)

Build logic to ensure that data from the various sources ‘speaks the same language’ and can be used for reporting purposes

Measure Actions and Outcomes – Begin by measuring operational tasks (completions of actions) and outcomes – how your organization performed on measures that are important to the business

Define the responsible parties and review cadence of ‘Summary Level’ analytics which define direction. Determine how data can be meaningfully inserted at the point of care.

• Define the list of metrics that leadership will manage by and define the cadence for which they will be created and reviewed

• Across Medical Cost Management, Quality, and Risk Adjustment, define the top metrics that impact overall performance

• Benchmark like staff against population to identify outliers and investigate need for additional training or remediation

Enable Physicians, Nurses, and Care Team Staff to take action by placing the relevant data in their hands at prescient times for enhanced monitoring and clinical decision-making

Test and Validate Hypotheses for how to improve outcomes – Predictive algorithms can be used to model the hypotheses, machine learning can be used to measure effectiveness and suggest better hypotheses

Package and Deliver subsets of data to staff at the appropriate moments with the appropriate insights – Integration with scheduling and customer centric data sets can enable staff with the right data at the right time

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value based care analytics - florida chapter of hfma€¦ · value based care analytics an overview...

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