Microsoft in Healthcare Analytics Georgia HIMSS

Microsoft in Healthcare Analytics

Georgia HIMSS

Martin SizemoreOctober 2, 2012

Perficient Recognized by Microsoft

• Microsoft Healthcare Provider Partner of the Year award winner for the 2nd time in 3 years.

• “Perficient combines deep health industry expertise with a high level of competency on Microsoft technologies. The result is a set of high value solutions that deliver value to the world’s leading healthcare enterprises,” Steve Aylward, general manager, US Health & Life Sciences, Strategies & Solutions, Microsoft.

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Microsoft Partner Landscape

12Global

SystemsIntegrators

33 National Systems

Integrators

500 Managed Partners

500,000 Gold Partners

Top 3 in the NSI Category.

Healthcare organizations are going through a technology and data revolution. Pressure from a wide range of sources are forcing both providers and payers to look at their data and technology investments in new and innovative ways:

– Use data to improve quality of care– Increase financial efficiency – Improve operational effectiveness– Conduct innovative research– Satisfy regulatory requirements

Perficient’s expertise and experience with full life-cycle BI Implementations, including the following disciplines:

– BI/Information Strategy– BI Design (User-Centered Approach)– BI Architecture & Enterprise Security (LDAP/SSO)– BI Implementation BI Education and Mentoring– Data Integration / Data Warehousing– Business Analytics and Reporting– Portal Integration

• Thousands of hours redirected to other tasks

• Enhanced analytics allow both in-depth and simple analytics

• Improved reporting and data mining capability

Business Intelligence & Analytics

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Meriter Health Systems

• Over 3,400 employees• Locations throughout southern Wisconsin• MHS includes:

– Meriter Hospital, Meriter Medical Group, Meriter Medical Clinics, Meriter Home Health, Meriter Laboratories, Meriter Foundation, Physician Plus Insurance Corporation

• Implemented a Cost Containment Dashboard• Integrated Clinical & Financial Systems• Utilized Microsoft Stack• RVU Based Compensation Reporting

Meriter Orthopedics Dashboard

DuPage Medical Group

• DMG is one of the largest physician-owned, multi-specialty groups in the Midwest

• DMG has more than 300 practicing physicians, 35 medical/surgical specialties and 40 locations

• Leveraged existing SQL Server infrastructure• Jump started an organizational transition towards using

standardized metrics• Encouraged “Self Service” Reporting• Provided Advanced Reporting and Analytical Capabilities

DuPage Medical Reporting

Agenda

1. Who is Predixion and what we do?2. Capabilities3. Use case based demo

Who is Predixion?

Predixion’s Vision

Predixion is a Predictive Analytics software company focused on healthcare

• Squeeze the complexity out of Predictive Analytics and Data Mining

• Enable the integration of Predictive Intelligence into all decisioning processes.

Retrospective vs. Prospective

How do we do better?

Predictive Analytics

How did we do?

Business Intelligence

Reactionary Healthcare The ACO Model

Easier to Create, Share and Consume

Insight Analytics™ - Easy data prep, model build and deployment

Predictively enabled: Dashboards, Applications, Reports

Easier to Create

Easier to Share

Easier to Consume

Innovating on “Ease-of-Use”

Analyst Creates Models

Enabling Non-IT Users

Integrated Predictive Intelligence

Management Views to better understand the patient population

Clinician Views to better predict individual patient outcomes

Operational Views that integrate predictive information into workflows

Predictive Solutions in Healthcare

•Predictable Readmissions•Chronic Condition Management•Co-Morbidity Identification•At Risk Patient Population Detection•Hospital Acquired Condition •Proactive LOS Monitoring•Blood Supply •Patient Satisfaction Scores

Use case name Overall goal: Use Predixion Insight to…

Preventing disease or disease progression

Decrease the incidence and/or progression of disease across covered population, improving member health and decreasing cost.

Manage population health

Score the probability of individual patients for developing a specific disease (heart disease, cancer, etc) or condition (osteoarthritis, obesity, depression) across covered population and deploy interventions shown to mitigate that disease process, thereby improving member health and decreasing cost.

***Predict readmission risk

Score the probability of readmission for currently admitted patients providing actionable information allowing the healthcare system to sensibly intervene to prevent those readmissions in a cost-effective and efficient manner.

Predict high-risk pregnancy risk

Develop a screening tool that allows payor to more accurately direct newly pregnant women at risk for complicated pregnancy to appropriate high-risk clinical setting. Specifically identify indicators for this condition to direct appropriate interventions.

***Length of Stay (LOS) estimation

Increase the accuracy and potentially automate the LOS estimation where possible.

Outlier/anomaly detection

Analyze various data sources to score from a probabilistic standpoint if outlying data points or anomalous trends are significant and further delineate underlying factors that are associated with those data points or trends.

Use Cases

Use case name Overall goal: Use Predixion Insight to…

***Overpayment Detection

Examine various data sets to determine if there is any pattern in claims data that could be accounted for by either fraud or error.

Hospital Acquired Illness

Score the probability of individual patient's developing a HAI and identify specific indicators that allow pro-active intervention to prevent this condition.

Chronic Condition Management

Score the probability of individual patients for developing a specific disease (heart disease, cancer, etc) or condition (osteoarthritis, obesity, depression) across covered population and deploying interventions shown to mitigate that disease process, thereby improving member health and decreasing cost.

***Fraud and Abuse Examine various data sets to determine if there is any pattern in claims data that could be accounted for by either fraud or error.

Membership Management

Develop probabilistic models that determine factors important to retaining subscriber membership or that lead to de-enrollment. Also, develop models that allow for proactive management of enrolled patient's health and resource utilization.

Provider Performance Measurement

Develop probabilistic models that score practioners likelihood to perform desirable actions that promote population health and/or influence appropriate resource utilization.

***Patient Satisfaction Scores

Use Cases

Predictive Intelligence - CMS Metrics Mapping

Patient/ Caregiver

Experience

1: Getting Timely Care, Appointments, and Information

2: How Well Your Doctors Communicate

3: Helpful, Courteous, Respectful Office Staff4: Patients' Rating of Doctor5: Health Promotion and Education6: Shared Decision Making

7: Medicare Advantage CAHPS, health Status/Functional Status

Care Coordinatio

n

Transition

8: Rate of readmissions within 30 days of discharge from an acute care hospital for assigned ACO beneficiary population

9: 30 Day Post Discharge Physician Visit

10: Reconciliation After Discharge from an Inpatient facility. Percentage of patients aged 65 years and older d/c from any inpatient facility and seen within 60 days

11: Uni-dimensional self-reported survey that measures the quality of preparation for care transitions

12: Diabetes, short-term complications (AHRQ PQI #1)

13: Uncontrolled Diabetes (AHRQ PQI #14)

14: Chronic obstructive pulmonary disease (AHRQ PQI #5)

15: Congestive Heart Failure (AHRQ PQI #8)16: Dehydration (AHRQ PQI #10)17: Bacterial pneumonia (AHRQ PQI #11)18: Urinary infections (AHRQ PQI #12)

Information

System

19: % All Physicians Meetings Stage 1 HITECH Meaningful Use Requirements

20: % of PCPs Meeting Stage 1 HITECH Meaningful Use Requirements

21: % of PCPs Using Clinical Decision Support

22: % of PCPs who are Successful Electronic Prescribers Under the eRx Incentive Program

23: Patient Registry Use

Preventative Health

26: Influenza Immunization27: Pneumococcal Vaccination28: Mammography Screening29: Colorectal Cancer Screening

30: Cholesterol Management for Patients with Cardiovascular Conditions

31: Adult Weight Screening and Follow-up32: Blood Pressure Measurement33: Tobacco Use Assessment and Tobacco Cessation Intervention34: Depression Screening

Patient Safety24: Health Acquired Conditions Composite25: Health Care Acquired Conditions: CLASBI Bundle

At-Risk Populations

Diabetes

35: Composite (All or Nothing Scoring) 36: Hemoglobin A1c Control (<8%)

37: Low Density Lipoprotein (LDL-C) Control in Diabetes Mellitus

38: Tobacco Non Use39: Aspirin Use40: Hemoglobin A1c Poor Control (>9%)41: High Blood Pressure Control in Diabetes Mellitus

42: Urine Screening for Microalbumin or Medical Attention for Nephropathy in Diabetic Patients

43: Dilated Eye Exam in Diabetic Patients44: Foot Exam

Heart Failure

45: Left Ventricular Function (LVF) Assessment46: Left Ventricular Function (LVF) Testing47: Weight Management48: Patient Education

49: Beta-Blocker Therapy for Left Ventriclar Systolic Dysfunction (LVSD)

50: Angiotensin-Converting Enzyme (ACE) Inhibitor or Angiotensin Receptor Blocker (ARB) Therapy for Left Ventricular Systolic Dysfunction (LVSD)51: Warfarin Therapy for Patients with Atrial Fibrillation

CAD

52: Composite: All or Nothing Scoring53: Oral Antiplatelet Therapy Prescribed for Patients with CAD54: Drug Therapy for Lowering LDL-Cholesterol

55: Beta-Blocker Therapy for CAD Patients with Prior Myocardial Infection (MI)

56: LDL level < 100 mg/dl

57: Angiotensin-Converting Enzyme (ACE) Inhibitor or Angiotensin Receptor Blocker (ARB) Therapy for Patients with CAD and Diabetics and/or Left Ventricular Systolic Dysfunction (LVSD)

Hypertension 58: Blood Pressure Control59: Plan of Care

COPD

60: Spirometry Evaluation

61: Smoking Cessation Counseling Received

62: Bronchodilator Therapy based on FEV1 Measures

Frail Elderly

63: Screening for Fall Risk;

64: Osteoporosis Management in Women Who had a Fracture

65: Monthly INR for Beneficiaries on Warfarin

Maps to Existing Solution Reuse of Existing Solution New Solution Potential New Solution

Identifying At-Risk Populations

I’m the 5%!

Risk Stratification with Predixion

A core ACO skillset for identifying and managing patient populations and driving proactive healthcare

500

300

125

Low Medium High

925

Total Population

Know who is at risk

92%26% 37%46%

23%74% 58%

Lay the foundation for Preventative Healthcare

Adapt care plans by risk strata

Improve efficiency and drive better patient outcomes

High Risk

Medium Risk

Low Risk

“Build the model”

Predixion Insight Ribbon in Excel

Identifying Key Influencers

Create the Model from Excel

Test the Model’s Accuracy

Data + Probability Score

Create ROI Analysis in Excel

“Deploy the model”the last mile of analytics

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Predixion’s Last Mile of Analytics

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Thank you

Microsoft in Healthcare Analytics Georgia HIMSS

Technology

Transcript of Microsoft in Healthcare Analytics Georgia HIMSS