PROMISE AND PERIL OF DATA ANALYTICS

Transcription

1 PROMISE AND PERIL OF DATA ANALYTICS David Muhlestein, PhD JD Senior Director of Research & Development Leavitt Muhlestein Becker s 7 th Annual Meeting April 27,

2 PRESENTATION OVERVIEW Understanding Data Using Data Challenges Recommendations 2

3 PROMISES OF DATA 3

4 THE HYPE OF DATA 4

5 WHAT CAN DATA REALLY DO FOR COMPANIES? Tests assumptions Keeps records, tracks growth Determines patterns Helps to inform decisions 5

6 COMMON APPROACHES TO USING DATA Exploratory/Descriptive Who, What, When, and Where? Provides information to develop hypotheses Ex: Prevalence & Incidence Analytic Why and How? Tests hypotheses developed using descriptive analyses Ex: Risk factors & Outcomes Also includes Diagnostic, Prescriptive, and Predictive analyses 6

7 Skill Levels TYPES OF ANALYTICS Diagnostic Analytics What is likely to happen next? Predictive Analytics Predictive Prescriptive Why did it happen? What should I do about it? Diagnostic Descriptive Descriptive Analytics What is happening? Prescriptive Analytics Value 7

8 SOUNDS GREAT, BUT HOW DO YOU DO IT? The Underpants Gnome business model.. unfortunately not a joke. Do Analytics Better Quality Lower Costs Happier Patients 8

9 EVEN SHAKESPEARE NEEDED PHASE 2 Paper & Pen Writing Works of Shakespeare 9

10 THREE PARTS TO USING DATA Phase 2 Collect Interpret Act Outcome EHRs Registries Claims Assessments Analytics Inform operational, population, and clinical strategies 10

11 UNDERSTANDING ANALYTICS: AN ANALOGY Sports = activities involving physical exertion and skill in which an individual or team competes against each other. 11

12 UNDERSTANDING ANALYTICS: AN ANALOGY Data Analytics = the science of examining raw data with the purpose of drawing conclusions about that information. Business/Operational Analytics Population Health Analytics Clinical Analytics 12

13 BUSINESS/OPERATIONAL ANALYTICS Using data to drive business planning. Data analytics entrance point Traditional business intelligence Examples: Processing reimbursement claims Establishing referral networks Managing and projecting capacity Budgeting and planning Identifying profit sources Customer relationship management Tracking the performance of marketing campaigns It would appear, Hopkins, that your gut feeling was simply indigestion. 13

14 POPULATION HEALTH ANALYTICS Aggregating patient data across multiple resources to understand your population. Next step in analytics No longer out of sight, out of mind it s out of sight, out of pocket Examples: Which patients have diabetes % of diabetic patients with A1C levels tested % of patients who are smokers ED use for patients with major depression 14

15 CLINICAL ANALYTICS Using data to inform the clinical practice of care. Improving the delivery of care on a 1-to-1 basis Actionable data in the hands of the physicians Examples: How to appropriately treat a diabetic with multiple comorbidities Diagnosing a patient in the exam room 15

16 SELECTING MEASURES Structure Nursing levels, Primary care evening hours Process Preventive screenings, Medication therapy management Outcome Patient satisfaction Patient-reported outcomes measures Clinical endpoint Discovery of local recurrence Functional endpoint Ability to walk 30-days post-surgery 3-part of goal of quality measures: 1. It can be accurately measured 2. It can be improved by the provider 3. Improvement on the measure means progress toward underlying goal 16

17 USING DATA 17

18 TRADITIONAL ANALYTICS What people have always done Informs business decisions Traditionally retrospective For example: Should we build a new hospital? What market share should we bring? What is the ROI of the project? What is the general trend with quality scores? Who are the high-volume physicians? 18

19 PREDICTIVE ANALYTICS What people would like to do in the future Based on current and past data Not commonly utilized For example: Which patients would benefit from a specific intervention? Which statin would be most beneficial for a CVD patient? Which doctor is most likely to have post-surgical complications? 19

20 CHALLENGES OF DATA 20

21 THREE PARTS TO USING DATA REVISITED Collect Interpret Act Outcome 21

22 CHALLENGE OF COLLECTING DATA Data collection practices vary Many different types of data Transactional (claims) Clinical Automated data (logs, locations, etc.) Data are often dirty Need for centralized data storage Inability/unwillingness of others to share data Regulatory barriers 22

23 CHALLENGE OF INTERPRETING DATA No training No time No reimbursement 23

24 CHALLENGE OF ACTING ON DATA Implementing into workflow Ensuring right people have right data at the right time Establishing a culture of trust 24

25 RECOMMENDATIONS 25

26 SO WHAT IS THE PERIL OF DATA ANALYTICS? Understanding Phase 2 26

27 THERE S NO MAGIC BUTTON 27

28 APPROACH 1. Understand your organizational goals 2. Clearly articulate what do you want to achieve 3. Align all interested parties in creating a datadriven solution 28