Big Data at the Dawn of Healthcare's New Age

Transcription

1 Big Data at the Dawn of Healthcare's New Age SD Fihn MD MPH Office of Analytics and Business Intelligence Veterans Health Administration Professor of Medicine and Health Services University of Washington Evolution of analytics in VA as an example Caveats and lessons June 2015

2 Veterans Health Administration m Enrollees 6.6m Patients Treated 92.4m Outpatient Visits 312k Outpatient Surgeries 707k Inpatient Admissions 21 Veteran Integrated Service Networks 1216 Sites of Care 178 Inpatient sites 167 Medical Centers 9 Residential Care 2 Extended Care 1038 Outpatient sites 14 Health Care Centers 187 Multi-Specialty 562 Primary Care 275 Other Other 59 Mobile Clinics 4 Vet Centers 70 Mobile Vet Centers 2

3 1 st Generation analytics HER data Data marts Performance measures Multidimensional cubes Dashboards Clinical alerts and reminders Plateaued effects Proliferation of siloed datamarts Limited access and filtering of data Alert fatigue and information overload Prevention Index Flu & pneumo vaccine Breast, cervical, CRC screening Tobacco & alcohol screening/intervention Lipid screening PSA counseling

4 Unintended Consequences: Inappropriate Care and Distracted Providers Kerr et al Arch Intern Med. 2012; 172: Although quantitative performance measurement systems have important benefits for patients, they may also lead to inappropriate clinical care, decrease providers focus on patient concerns and patient service, and compromise patient education and autonomy. These negative unintended consequences are largely determined by facility-level strategies undertaken to improve PM scores. Powell et al, J Gen Intern Med 2011;27:

5 2 nd Generation Analytics Consolidated/ linked data sources Enhanced data display and web-based reporting Search engine Data Type TOTAL ENTRIES Entries per Day Orders 3.5 billion 1.2 million Images 3.7 billion 2.8 million Text (H&P, Notes) 2.3 billion 1.0 million Medications 1.9 billion 0.6 million Vital Signs 2.5 billion 0.9 million CDW: 1.5 petabytes, 900+billion rows of data; 30,000 users/mon; 840+ tables 5

6 >900 data-related products in active use Enterprise Wide Search Portal for Performance Data 6

7 Productivity, Efficiency and Staffing 7

8 Veteran Experience and Satisfaction National AVG 90 th P-tile 10 th P-tile Access Composite: Primary Care Thru May Overall Rating of Primary Care Provider Thru May Overall Rating of Hospital Stay Thru Apr

9 Summary Reports 9

10 Proliferation of Measures in Health Care Vital Signs: Core Metrics for Health and Health Care Progress. David Blumenthal, Elizabeth Malphrus, and J. Michael McGinnis, Editors; Committee on Core Metrics for Better Health at Lower Cost; Institute of Medicine. Institute of Medicine, 2015

11 How much do the following limit your ability to provide optimal, patient-centered care? (n=6,467) Proportion of respondents who say limits a great deal Clinical reminder volume 43.30% Recruiting and retaining providers 41.80% Lack of control over my schedule 38.80% CPRS view alerts volume (e.g., provider-to-provider communication) 34.40% Difficulty accessing specialty care 34.00% Recruiting and retaining non-provider clinicians 33.60% Poor communication with specialists within VA 32.60% Inadequate time allotted to provide counseling or education 32.40% Time & effort to input notes 32.30% Inadequate time allotted to provide follow-up care 31.90% Poor communication with specialists outside the VA 31.30% Recruiting and retaining non-clinicians 31.10% Adoption of EHR 90 min of additional work per 8 hrs of patient care

12 3 rd Generation Desktop BI Democratization of information Improved display Data exploration Advanced analytics High-level modeling Program Assessment Platform independence Analytic platform for population health Integrating analytics into workflow

13 Desktop BI 13

14 Care Assessment Need (CAN) Score Reflects Indicates how a given patient compares with other VA patients in terms of likelihood of hospitalization or death 14

15 Predictive Analytics: Care Assessment Need (CAN) Score Demographics Utilization Chronic Illness Pharmacy Age Group Air Force Flag Eligibility (1, [2-4], 5+) Rank Flag (Officer vs Enlisted) Marital Status Priority (Max) Sex Vital Signs BMI ( 40) Weight Variability HR (80-60) Resp Rate ( 20) Sys & Dias BP No. Hospital/Bed Days No. Medical Providers No. Visit Type: All Inpatient Emergency Care Cardiology CT Mental Health Other Non-Face Primary Care (PC) Phone Care PC Phone Care No min Phone No min Phone No. Est Office Visit Deyo-Charlson Score HCCs: AFib and CHF Dementia Mental Health and PTSD Metastatic Cancer Alcohol Chronic Airway Obstruction Lab/Radiology No. Albumen No. Blood, Urine, Nitrogen Lymphocytes (Low) Red Blood Cells (Low) Sodium (Low) White Blood Cells (High) No. Troponin No. Chest X-Ray Antipsychotic Beta-blocker Benzodiazepine Beta Agonist Nebulizer Furosemide Statin Metformin NSAID No. of Drugs Filled Furosemide Tablets Text Notes No. Consent Notes No. Telephone Notes

16 Predictive Accuracy Probability of admission or death within (90d or 1yr) expressed as percentile [0 (lowest risk) to 99 (highest risk)] Hospitalization - 1 year 0.81 Hospitalization - 90 day 0.83 Mortality - 1 year 0.85 Mortality - 90 day 0.87 Veterans in highest %ile of risk have 62% probability of admission, 30% probability of death, and 72% probability of either event.

17 Updated Weekly

18 CAN USAGE FY No. of CAN users, cal yr 2014, by month No. of CAN report hits, cal yr 2014, by mon. 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec January February March April May June July August September October November December May underestimate usage because nurse care managers download data for several providers & data can be downloaded directly from regional data warehouses 18

19 Few Patients with High Scores Referred to Coordination Programs: Telehealth, Home-Based Primary Care, Palliative Care, and Hospice Palliative Care Score ,353 of 241,917 total patients (0.6%) Hospice Score of 241,917 total patients (0.2%)

20

21 Use of High Level Analytic Data for Population Management and Resource Planning 1-yr likelihood of admission or death 1-yr likelihood of admission 1.86% % 5.94% % 7.01% % 7.98% % 9.22% % 2.37% % 9.04% % 10.02% % 10.97% % 12.19% % Fihn, et al Health Affairs

22 Current Predictive Modeling Activities Kidney Disease Risk of acute kidney injury at admission, before/after cardiac catheterization Risk of dialysis (6 mos. and 5 yr.) among patients with chronic kidney disease Cardiovascular Risk of stroke with atrial fibrillation Risk of coronary event or stroke within 5 years Infectious Disease Risk of drug-resistant Klebsiella infection Risk of infx. w/ mult. drug resist. organism (Acinetobacter, E coli, Pseudomonas) Risk of C. diff infection Mental health: Risk of suicide, ER visit, drug OD or mental health admission Response of prostate cancer to chemotherapy 22

23 PACT IMPLEMENTATION PROGRESS INDEX (PI 2 ) Domains # of items Source of data Access 11 Patient surveys Continuity of care 3 (CAPHS-PCMH) & Coordination of care 8 CDW Team-based care [delegation, staffing, team function] Comprehensiveness 3 Self-management support 2 Patient-centered care and communication Shared decision making 2 18 Provider survey 6 53 Patient surveys (CAPHS-PCMH) Nelson et al. JAMA Intern Med

24 Pi 2 Scores, Patient Satisfaction, Provider Burnout PI 2 scores* No. of clinics Patient satisfaction (0 worst 10 best) Provider rating, CAHPS-PMCH Overall health care rating, SHEP Provider Burnout MBI** ED visits/ 1000 pts 5 to to to to to *Pi 2 score = number of domains in top and bottom quartiles for the domain scores, range 8 (all domain scores in top quartile) to -8 (all domain scores in bottom quartile ). **MBI emotional exhaustion scale Also significantly fewer visits for Ambulatory Care Sensitive Conditions 24

25 Higher implementation sites had better clinical quality Clinical quality indicators were significantly higher at sites with the most effective implementation (proportion of patients meeting each indicator) 2012: 19/48 indicators 2013: 17/48 indicators 25

26 Mobile Data 26

27 Clinical Assessment, Reporting and Tracking System Cardiac Catheterization Labs Standardized data capture at all VA Cath Labs Natl data repository Totals through May 22, 2015 No. of Unique Patients ,155 No. of Unique Providers -- 5,557 Reports CATH+PCI ,353 Assessments ,686 Total Number of Reports - 797,891 Box, et al. J Gen Intern Med. 2010;25 Suppl 1:72-6.

28 CART Initiatives Patient Safety Device Safety monitoring Medication adherence clopidogrel Bleeding risk Risk for contrast-induced kidney injury Quality/Value Appropriateness -- National Peer Review Patient reported outcomes -- Stent selection Occupational safety Radiation exposure monitoring Asset management RFID tracking

29 Convergence of Quality Improvement and Scholarship Facility-Level Variation in Hospitalization, Mortality, and Costs in the 30 Days Following Percutaneous Coronary Intervention: Insights on Short-Term Healthcare Value from the VA CART Program Steven M. Bradley, MD, MPH;Colin I. O Donnell, MS; Gary K. Grunwald, PhD; Chuan-Fen Liu, PhD; Paul L. Hebert, PhD; Thomas M. Maddox, MD, MSc; Robert L. Jesse, MD, PhD; Stephan D. Fihn, MD, MPH; John S. Rumsfeld, MD, PhD; P. Michael Ho, MD, PhD 29

30

31 Standard Orders 31

32 Developing Context-Sensitive Orders 30 million records 20 years of data mined to identify relationships between Problems Laboratory results Prescriptions Procedures Orders Location Provider Patient 15,000 conditions and 10,000 orders

33 Common Test and Drug Orders Epigastric pain CBC with diff Chemistry panel Helicobacter pylori Lipase Amylase Upper GI endoscopy Abdominal ultrasound CT abdomen w/o contrast Omeprazole Ranitidine Amphogel Rabeprazole Promethazine Sucralfate Metronidazole Metoclopramide Docusate

34 34

35 Data Tsunami 35

36 Some Problems Associated with Performance Measurement Key data often missing, in unstructured text, inconsistent over time Difficulty specifying a workable definition of quality Challenges determining correct levels & locations of measurement Information not timely, relevant, within workflow Problems interpreting the significance of change w/o adjustment Distrust of measurement by those being measured and the public Development of target fatigue Gaming activities resulting in target myopia Hitting the target but missing the point Bayley et al, Med Care 2013; 51:S80 Pflueger D BMC Hlth Svcs Res 2015;15:178 36

37 Overlap Of Hospitals Rated As High Performers And Low Performers Among 4 Rating Systems No. rated as high No. of ratings that overlap performers High performers On all 4 ranking systems 0 On 3 of 4 ranking systems 3 On 2 of 4 ranking systems 85 Low Performers On all 3 ranking systems 0 On 2 of 3 ranking systems 15 Leap Frog Group (n=2,514) Consumer Reports (n=2,040) US News (n=1,928) Health Grades (n=262) Austin JM Hlth Aff 2015:

38 Cognitive Biases when using Numbers We see patterns in randomness (clustering illusion) We see what we want to see (confirmation bias) We see what we re used to seeing (availability bias) We extrapolate more than we should We conservatively bound possible outcomes We underestimate the likelihood of randomness We overestimate the likelihood of rare events and underestimate the likelihood of common ones Gilovich, How We Know What Isn t So VHA Office of Informatics and Analytics

39 Excessive Reliance on Performance Measurement Can Lead a System Astray the Army as an institution is quick to pass down requirements to individuals and units regardless of their ability to actually comply with the totality of the requirements.. subordinates are forced to prioritize which requirements will actually be done to standard and which will only be reported as done to standard. leading truthfully may include striving for 100% compliance in all areas, but being satisfied when only 85% is reported in some

40 Lessons Need for robust data systems Build purposefully and incrementally Importance of data linkages Meaningful integration of data collection and reporting into routine work flow Provide value to workforce and organization Information and organizational governance Maintain information standards Differentiate process improvement and accountability