PSCI Case Study

7/27/2019 PSCI Case Study

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Leading Physician Network lowers PerMember Per Month (PMPM) costs by

reducing acute care admissions forchronic disease conditions through

effective care management


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Patient centric medical home (PCMO )

leverages unique state-of-health population

risk stratification approach from PSCI.

PCMO uses Population Predictive Risk Analytics from PSCI.


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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.


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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.


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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.


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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.


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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.


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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.


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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.


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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:


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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.


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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.


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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.


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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


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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.


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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


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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.


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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.


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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.


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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.


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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


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RESULTS


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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.


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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.


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Population Risk Stratification


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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

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RESULTSCustomized Care Management Programs


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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


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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


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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.


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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/

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PSCI Case Study

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