MHS Population Health Analytics: March 2, 2016

Transcription

1 MHS Population Health Analytics: March 2, 2016 Col (s) David Carnahan, MD, MSCE Chief, Enterprise Intelligence Branch Information Delivery Division Health Information Technology Directorate Defense Health Agency Jesus J Caban, PhD National Intrepid Center of Excellence (NICoE) Walter Reed National Military Medical Center Bethesda, MD

2 Disclosure Col (s) David Carnahan, MD, MSCE has no real or apparent conflicts of interest to report. Jesus J Caban, PhD has no real or apparent conflicts of interest to report. 2

3 Agenda MHS Population Health Timeline MHS Population Health Lessons Learned MHS Population Health Spotlight: Traumatic Brain Injury Advanced Analytics for Traumatic Brain Injury 3

4 History of Population Health in the Military Health System (MHS) 4

5 Population Health in the MHS AF Content Latency Distribution Other Registries Quarterly Mailed CDs 5

6 Population Health in the MHS AF Content Latency Distribution Other Registries HEDIS Quarterly CDs Online / Website Stratified Basic Counts 6

7 Population Health in the MHS 1999 AF A Content Latency Distribution Other Registries, HEDIS Prevalence (LBP) Quarterly Monthly CDs Online / Website Stratified Basic Counts Ability to Download 7

8 Population Health in the MHS AF A N Content Latency Distribution Other Registries, HEDIS Prevalence (LBP) Colorectal Screening Quarterly Monthly CDs Online / Website Stratified Basic Counts Ability to Download 8

9 Population Health in the MHS AF A N Content Latency Distribution Other Registries, HEDIS Prevalence Quarterly Monthly CDs Online / Website TROs Stratified Basic Counts Ability to Download Funded by TMA 9

10 Population Health in the MHS AF A N Content Latency Distribution Other Registries, HEDIS Prevalence ACG Predictors Quarterly Monthly Daily CDs Online / Website TROs Stratified Basic Counts Ability to Download Funded by TMA 10

11 Population Health in the MHS AF A N Content Latency Distribution Other Registries, HEDIS Prevalence, ACG, CDS Alerts, RR Quarterly Monthly Daily Real-time CDs Online / Website TROs Stratified Basic Counts Ability to Download Funded by TMA Separated Logic 11

12 Population Health in the MHS AF A N 2015 Content Latency Distribution Other Registries, HEDIS Prevalence, ACG, CDS alerts, RR Quarterly Monthly Daily Real-time CDs Online / Website TROs Stratified Basic Counts Ability to Download Funded by TMA Separated Logic Tagging, Chats 12

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14 Lesson #1: Patience & Humility Recognize the Five Stages of Data Grief: 1. This can t be right! Your analysis, code, visualization must be wrong. 2. Who is responsible for these errors? Why haven t we seen this before? 3. Can we modify the methodology? Can you tweak the visualization? 4. This dataset is worthless. There s no point trying to capture this data any more. 5. We know there are some problems with the data. We ll use it, and describe the limitations of the analysis. 14

15 Lesson #2: Responsiveness Mitigates Imperfections The MHS Population Health Portal guiding principles: 1. Data Accuracy is Pre-eminent.* 2. System Performance is Fast and Reliable. 3. Usability is critical. *Data Code Blue 15

16 Lesson #3: Tuckman was right 1. Forming 2. Storming 4. Performing 3. Norming Tuckman et al. Group Organization Management, December 1977 vol. 2 (4)

17 Lesson #4: Change Happens in Real Time Exploratory Predictive Prescriptive Age Chronic Condition LOS Gagne Score PC F/U* RUB DRG Group ICU Stay Age Chronic Condition Gagne Score RUB 16% of Admissions 40% of Readmissions Comparative Effectiveness Studies PC F/U DC Plans Home Hlth PC F/U Pharm DC PC F/U 17

18 MHS Population Health Spotlight Traumatic Brain Injury 18

19 Traumatic Brain Injuries Glasgow Coma Scale (GCS) Post-traumatic amnesia (PTA) Loss of consciousness (LOC) Mild < 24hrs 0-30 mins Moderate 9 12 >1 to <7 days >30mins <24 hrs Severe 3 8 >7 days >24 hrs Mild Traumatic Brain Injury (mtbi) (a.k.a. concussion) an invisible injury that is poorly understood and difficult to evaluate not a single measurement to evaluate mtbi 1. Structural imaging: normal 2. Loss of consciousness (LOC): 0-30min 3. Alteration of consciousness: < 24hrs 4. Post-traumatic amnesia (PTA): 0-1 day 5. Glasgow coma scale:

20 Mild Traumatic Brain Injury (TBI) In the US, the annual incidence of sports-related brain injuries is estimated over 300,000 a year 1 Over 313,816 service members (SMs) have sustained a traumatic brain injury (TBI) % were classified as mild TBI 1 Traumatic Brain Injury in the United States: Emergency Department Visits, Hospitalizations, and Deaths, DCoE, DoD worldwide numbers for TBI

21 Challenges & Limitations Imaging LOC AOC PTA GCS Pathophysiological State of mtbi Patients Severity Rating for TBI HYPOTHESIS Understanding the effects of mtbi for a given population is (in part) a data and HealthIT issue. 21

22 What s needed? In order to successfully understand the complex interactions of mtbi, there are two areas that need to improve: Data Large and Comprehensive datasets Multi-disciplinary data Structured and unstructured data Analytical Models Multimodal analytical techniques Advanced data mining Predictive models 22

23 Data Ecosystem for mtbi MHS Population Leverages DHA HIT systems such as HSDW and CarePoint. 23

24 Example 1: Modeling Clinical Trajectories 24

25 Clinical Trajectories Patient #1 Encounter 1 Concussion Headache Pain Encounter 2 Fever Sore throat Encounter 3 Headache Sleep Disorder Encounter 4 Rash Skin Cancer Screening Encounter 5 Sleep Disorder Anxiety Depression Encounter 6 PTSD Anxiety Depression t 1 t 2 t 3 t 4 t 5 t 6 25

26 Sample Path between mtbi to PTSD D depression N neuro k nonskull_fracture P ptsd S sleep_disorder T Vcode 26

27 Ten Individual Paths - mtbi to PTSD 27

28 Modeling Clinical Trajectories & Predicting Future conditions Model #1 Model #2 Model #3 t -M... t -1 t 0 t 1 t 2... t N PTSD Sleep Headaches Anxiety Depression mtbi/concussion t -M... t -1 t 0 t 1 t 2... t N 28

29 Predictive Analytics for mtbi W1 The accuracy of the results increase when observation window uses one year worth of data W2 29

30 Example 2: Subcategories of mtbi 30

31 Classification of TBI Glasgow Coma Scale (GCS) Post-traumatic amnesia (PTA) Loss of consciousness (LOC) Mild < 24hrs 0-30 mins Moderate 9 12 >1 to <7 days >30mins <24 hrs Severe 3 8 >7 days >24 hrs Mild Traumatic Brain Injury an invisible injury that is poorly understood and difficult to evaluate not a single measurement to evaluate TBI/PH 82.4% of injuries classified as mtbi (a.k.a. concussion) Q: How can we effectively determine subcategories of mtbi patients? 31

32 Sub-classification of mtbi Is the symptomology of mtbi patients similar? Describe each patient P i as a multi-dimensional vector v i of symptom. Patient P i Patient Representation x 11 x 12 x 13 x 14 x 21 x 22 x 31 x 32 x 33 x 34 x 41 x 42 x 43 x 51 x 52 x 53 x 54 Neurological Behavioral Physical Cognitive 32

33 Sub-classification of mtbi Dataset: N = 594 Cross-validation (66/33) 33

34 Per-cluster Symptom Improvement C1 C2 Admission Discharge C3 C4 34

35 Example 3: Visual Analytics for MHS Population Health and mtbi 35

36 Visual Analytics for MHS Population Health Visualization and visual analytics show great potential as methods to analyze, filter, and illustrate vast amount of electronic medical data. Provide visual representations of datasets designed to help people carry out tasks more effectively Method to present the data and help unlock the hidden patterns for different groups Clinicians Researchers Patients Administration 36

37 Tracking New mtbi Events 37

38 Visualizing Clinical Trajectories 38

39 Longitudinal Clinical Trajectories 12 months before mtbi 12 months after mtbi 39

40 Conclusion By leveraging DHA systems, DoD currently has a comprehensive database of mtbi patients. Currently researchers extracting new knowledge from the data by applying advanced analytical techniques, predictive models, and visualization techniques. 40

41 Thank you! 41

42 Contact Information David Carnahan, MD, MSCE Chief, Enterprise Intelligence Defense Health Agency E: Jesus J Caban, PhD Chief, Clinical & Research Informatics NICoE, Walter Reed Bethesda E: jesus.j.caban.civ@mail.mil 42