psci case study
TRANSCRIPT
-
7/27/2019 PSCI Case Study
1/34
Leading Physician Network lowers PerMember Per Month (PMPM) costs by
reducing acute care admissions forchronic disease conditions through
effective care management
-
7/27/2019 PSCI Case Study
2/34
Patient centric medical home (PCMO )
leverages unique state-of-health population
risk stratification approach from PSCI.
PCMO uses Population Predictive Risk Analytics from PSCI.
-
7/27/2019 PSCI Case Study
3/34
PCMO SITUATION The successful patient centric medical home
(PCMO) is a leading provider ofPrimary Caremanagement services and is known for itsnetwork of outstanding physicians in the local
market. The innovative, growth-oriented management
team made the decision to proactively acquirethe capabilities required to prosper in theemerging climate ofpay-for-performance.
-
7/27/2019 PSCI Case Study
4/34
OPPORTUNITY The ACO, bundled payment and pay-for-
performance models require transformationalprocess improvements in the primary care settingto avoid unnecessary hospitalizations and ERvisits.
The PCMOs growth strategy was to offer thelocal leading self-insured employers a compellingvalue proposition with their focus on preventive
care and chronic care management, to minimizethe total cost of care to their membership acrossthe continuum-of-care.
-
7/27/2019 PSCI Case Study
5/34
OPPORTUNITY The value proposition needed to be credible
and measurable in order to negotiate higherrates for physician services and also increase
market share in the local market.
-
7/27/2019 PSCI Case Study
6/34
CALL TO ACTION After careful analysis of their patient
population healthcare costs, it was clear thatthe highest cost population category was
chronic disease care and unnecessary ER
visits.
-
7/27/2019 PSCI Case Study
7/34
-
7/27/2019 PSCI Case Study
8/34
CALL TO ACTION To accomplish this, they needed analysis tools
to continuously identify and monitor highrisk patients proactively by major chronic
condition along with the risk drivers.
They also wanted decision support tools to
measure patient risk based on current state
of health using clinical data from theirexisting EMR systems on a monthly basis.
-
7/27/2019 PSCI Case Study
9/34
CALL TO ACTION High risk chronic patients were defined as
those with a high probability for admission toacute care facilities within the next 12-18months due to complications.
Furthermore, the team wanted physicians tohave the ability to analyze which processeswere needed to fill any gaps in caremanagement that may lead tohospitalizations.
-
7/27/2019 PSCI Case Study
10/34
CALL TO ACTION The required tools had to be
comprehensive yet provide easy-to-
absorb information with a clinicalperspective.
The client insisted that physicians be ableto quickly and easily identify the key risk
drivers and prescribe appropriate careand case management programs atpatient and population levels.
However, the client were adamant thatthese tools not be used for physicianprofiling or as clinical outcomepredictors.
-
7/27/2019 PSCI Case Study
11/34
THE CHALLENGE The team searched the market for a vendor to
provide decision support tools. They reviewedrisk adjustor applications, and determined thetool did not adequately meet their requirements.
Furthermore, the evaluation team learned thatmost risk adjustment tools were primarily built toaddress payer needs.
They reported that claims-based risk predictortools did not serve their objectives for thefollowing reasons:
-
7/27/2019 PSCI Case Study
12/34
THE CHALLENGE Acute care cost centric Risk adjustor models
are extremely complex and heavily skewed toacute care costs and past resource utilization.
Models incorporate many variables that are
cost-focused and not under primary care
management control.
-
7/27/2019 PSCI Case Study
13/34
THE CHALLENGE Claims-based Models are heavily based on
claims data with a payer-centric perspective,whereas the physicians wanted clinical-centricmodels.
These models are very controversial and havea negative connotation with clinical teamsbecause they are commonly used for physicianprofiling.
-
7/27/2019 PSCI Case Study
14/34
THE CHALLENGE Cost-prohibitive These tools are very
expensive and it is difficult to interpret resultsfrom a care management perspective. Near
real-time analysis with weekly/monthly
frequency is prohibitively expensive.
-
7/27/2019 PSCI Case Study
15/34
-
7/27/2019 PSCI Case Study
16/34
THE CHALLENGE These models perform regression analysis at a
population level, then attempt to take scores to apatient level.
Risk scores at patient levels were based on relative
scores aligned with the population, thereforeindividual patient scores would vary with population
changes, with no change in the individual state of
health.
It was difficult to interpret the clinical drivers and
their impact on the risk scores
-
7/27/2019 PSCI Case Study
17/34
THE DECISIONThe evaluation committee realized
that risk adjustor tools were notbuilt to address primary care
provider-driven care management
programs. The team decided tobuild an application in partnership
with an innovative healthcare
decision support provider.
-
7/27/2019 PSCI Case Study
18/34
-
7/27/2019 PSCI Case Study
19/34
PSCI, with the help of clinical teams, conductedextensive research and identified nationally accepted
state-of-health models for each major chroniccondition to start with.
PSCI developers worked with physician teams to make
the models more pragmatic in context of availabledata, with standardized assumptions, andsimplification in agreement with larger expert teams.
The solution collected clinical data from existing
ambulatory EMR, lab, pharmacy, and claims systemson a regular basis to refresh patient state-of-healthrisk scores.
THE APPROACH
-
7/27/2019 PSCI Case Study
20/34
PSCIs EMR-based Population Risk Predictive Model
PSCI uses a patent pending, transformationalapproach for predicting risk of hospitalization that
takes into account 6 dimensions. No one in the
industry has put all of them together to predict
risk of hospitalization/re-admission.
-
7/27/2019 PSCI Case Study
21/34
THE APPROACH Calculate patient state-of-health scores by
chronic disease condition for the mostcommon chronic conditions for the target
population mix using latest patient records
from EMR
The score would indicate the probability of
hospital admission for any given patient dueto complications within 12-18 months.
-
7/27/2019 PSCI Case Study
22/34
-
7/27/2019 PSCI Case Study
23/34
THE SOLUTION Identify evidence-based best practices based
on data analysis and physician input for eachchronic condition.
Provide insight and data for optimal care-management programs for patient risk groups.
-
7/27/2019 PSCI Case Study
24/34
THE SOLUTION Help physicians maximize pay-for-
performance and Shared Savings Model(ACOs) and help physicians proactively
manage patient population risk.
Not a point-of-care solution.
Not an outcome prediction tool.
-
7/27/2019 PSCI Case Study
25/34
Provides easy-to-understand risk scoredrivers, and pinpoint which variable(demographic, clinical, etc.) is contributingto an adverse state-of-health at any given
time. Physicians and clinical teams then
determine what diagnosis, treatments,
and care management strategies to focuson to improve the specific patient riskscores.
THE SOLUTION
-
7/27/2019 PSCI Case Study
26/34
RESULTS
-
7/27/2019 PSCI Case Study
27/34
RESULTSPSCI delivered Population Risk Analyzer, a care
management decision support tool that: Helped in reduction of hospitalizations & ER
visits with an increase in case manager andcare manager productivity.
-
7/27/2019 PSCI Case Study
28/34
RESULTS Provides a state of health risk score for each
chronic condition for a patient or a populationbased on current clinical information.
The risk scores are calculated at the patient level
and then rolled up to the population level. The solution enables physicians and
administrators in their local setting ACOs, clinics
in an integrated health care system, etc. to lookat the information and identify clinically high-riskpatients ER visits/hospitalization/readmissions.
-
7/27/2019 PSCI Case Study
29/34
Population Risk Stratification
-
7/27/2019 PSCI Case Study
30/34
9 Target right patients (High Risk Patients) at right time
9 Strong individualized care management programs
9 Intensive, multi-level, multi-dimensional, high contact programs
9 Provider-driven programs
9 Broad programs have no impact
9 Data-driven care management analytics
16
RESULTSCustomized Care Management Programs
-
7/27/2019 PSCI Case Study
31/34
BlueCross BlueShield has been running
medical home pilots since 2010 with Village
Health Partners in Plano and the 42 offices ofthe Medical Clinic of North Texas. The pilots
improved care and saved an average of
$10.50 a month for 25,000 patients, saidScott Albosta, a division vice president with
the insurance company. - (Dallas Morning
News June 23, 2012).
OUTCOMES
-
7/27/2019 PSCI Case Study
32/34
By using near real-time patient healthrecords from EMRs along with financial
claims and demographics data, PSCIpresents clinical teams information thatallows them to understand the risk driversassociated with patient care across thepatient population. By understanding theclinical cost, quality and risk drivers,physicians make interventions to have a
dramatic impact to lower the healthcarecost curve.
Karen Kennedy, CEO Medical Clinic ofNorth Texas
TESTIMONIALS
-
7/27/2019 PSCI Case Study
33/34
ABOUT PSCI PSCI is an innovative provider of predictive
population risk analytics for care management andcontract optimization leveraging EMR, Claims &
Demographics data for medical homes, physician
groups, ACOs, hospital systems, IDNs, and sharedsavings programs.
-
7/27/2019 PSCI Case Study
34/34
ABOUT PSCI PSCI delivers predictive chronic disease models for
population state-of-health risk stratification, quality-cost-risk visibility, "what-if" modeling and ACO
demand planning for improving overall healthcare
provider and payer performance. PSCI is critical to managing At-Risk populations and
pay-for-performance objectives. For more
information, please visit http://www.PSCIsolutions.com
http://www.pscisolutions.com/http://www.pscisolutions.com/