MeDSS: a Data-Driven Tool for Pain Management

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1 1 MeDSS: a Data-Driven Tool for Pain Management Jaya Tripathi Scott Weiner, MD, MPH The International Conference on Opioids, Jun 11, 2017

2 2 Presentation Abstract Appropriate pain medication management remains a challenge for clinicians who wish to both adequately treat pain but also avoid prescribing to patients with high risk of overdose. Using advanced analytical and visualization techniques on longitudinal prescription data, we created a system that quickly reports potential abusive users to the clinician at the POC, in order to improve prescribing decision-making The MITRE Corporation. ALL RIGHTS RESERVED. Approved for Public Release. Distribution Unlimited. Case Number

The Extent of the Opioid Problem 3 Cause of Death Number Rate* Major dopamine pathways Diabetes Mellitus 76,488 24 Chronic liver disease and cirrhosis 38,170 12 Motor vehicle accidents 35,398 11.

3 The Extent of the Opioid Problem 3 Cause of Death Number Rate* Major dopamine pathways Diabetes Mellitus 76, Chronic liver disease and cirrhosis 38, Motor vehicle accidents 35, Drug-induced deaths 49, Multiple Causes of Mortality Data * per 100, % increase in opioidrelated deaths from there were 33,091 deaths from opioids in 2015; 15% suicide, rest unintentional THE COST $72 billion in 2011 for opioid abuse alone!

4 Opioid-Related Overdose Death (CDC)

5 Opioid-Related Overdose Death (CDC)

6 Many Efforts to Address the Problem One Such Effort: The Prescription Drug Monitoring Programs (PDMPs) Data Sources of Pharmaceutical Transactions of Controlled Substances Promote Public Health And

6 6 Many Efforts to Address the Problem One Such Effort: The Prescription Drug Monitoring Programs (PDMPs) Data Sources of Pharmaceutical Transactions of Controlled Substances Promote Public Health And Welfare as a Resource for Detecting Misuse and Abuse Commissioned in 49 States Prescriber use mandated in 35 states Increased Data Sharing Between States Via Data Hubs to Track Cross-border Trafficking

7 7 Prescription Drug Monitoring Programs (PDMPs) Present in many states for several years Novelty: Online databases that practitioners can log in to prior a prescription Various timeframes 2 months to start of database Data sharing

8 8 CURRENT: Prescriber Decision-Making

9 9 Common sources of complaints in the ED 1. Abdominal PAIN 2. Chest PAIN 3. Fever 4. Headache PAIN 5. Cough 6. Back PAIN 7. Shortness of Breath 8. Complaint of PAIN not referable to another site 9. Throat PAIN 10. Vomiting 52% of all ED visits present with pain National Hospital Ambulatory Medical Care Survey: 2011 Emergency Department Summary Tables Cordell, et al. The high prevalence of pain in emergency medical care. Am J Emerg. Med May;20(3):165-9.

10 10 63% 73% 41% PDMP changed plan in 9.5% patients: 3.0% no longer receiving an Rx 6.5% now getting an Rx EP sensitivity EP specificity EP PPV

11 11

12 Subjective Interpretation: I Know It When I See It 12 Physicians gestalt is not good in determining drug-seeking behavior, and providers who look at the same exact profile interpret it in different ways. Even when faced with identical case scenarios, physicians decisions to prescribe opioid analgesics are highly variable. PDMP data interpretation affects the decision to prescribe opioids, however, there is variability in which profile factors affect the decision Tamayo-Sarver (Ann. Emerg. Med. 2004;43: ) Exact same 3 scenarios to 398 physicians Hoppe and Weiner (Academic Emergency Medicine, 2015; vol. 22, #26) 311 emergency physicians were given 4 PDMP profiles

13 Uncontrolled Variation is the Enemy of Quality 13

14 Uncontrolled Variation is the Enemy of Quality 14

15 15 PDMP Patient Data Sample Screenshot Go over this spreadsheet and interpret it all in a couple of minutes?!

16 16 The Evidence: Putting it Together Peirce (Med Care 2012;50: ) Patients who died of overdose compared with living controls 25% vs 4% doctor shoppers > 3 prescriptions in 6 months Gwira-Blamblatt (JAMA Int Med 2014;174: ) Opioid-related deaths in Tennessee > 3 prescribers in 1 year OR 6.5 > 3 pharmacies OR 6 > 100 MME*/day OR 11.2 Jena (BMJ 2014;348:g1393) Increased number of opioid prescribers Increased number of hospital admissions * Morphine Milligram Equivalent

17 17 The Evidence: Putting it Together Paulozzi (Pain Med 2011 Jan;13(1):87-95) Male sex: OR 2.4 One or more sedative/hypnotics: OR 3.0 Number of Rx: OR 1.1 for each additional Rx Buprenorphine: OR 9.5 Fentanyl: OR 3.5 Hydromorphone: OR 3.3 Methadone: OR 4.9 Oxycodone: OR 1.9 >40 MME/day: OR 12.2

18 18 High Level Risk Assessment 3 Rx per year no early refills no psychiatric comorbidities no concurrent benzos no self pay > 100 MME opioids per day long acting opioid (methadone, oxycontin, MS- Contin, Fentanyl patch buprenorphine MME per day Opioid + benzo > 4 providers in 12 months > 4 pharmacies patient pay early refills

data Medical knowledge Epidemiological data

19 19 Clinical Decision Support Systems designed to link patient specific information within the EHR (electronic health record) and a knowledge base to generate case specific guidance. Everything in one place Actionable Patient data Medical knowledge Epidemiological data Inference engine Patient specific recommendation Courtesy of Jason Hoppe, DO

20 20 Clinical Decision Support: Process Input PDMP data Clinical data Inference engine Evidence based medicine, rules, algorithms, or clinical guidelines Output Synthesized, easily digestible presentation at appropriate time

21 Emergency department: Passive 21 Input Provider decides when to search PDMP Searched Analysis PDMP raw data interpreted based on clinician knowledge, experience and situation Output Decision on whether to prescribe +/- Discussion with patient

Emergency department: Active 22 Input Patient registers Demographic data collected PDMP request sent returns before clinical eval Analysis PDMP data and clinical data evaluated with respect to

22 Emergency department: Active 22 Input Patient registers Demographic data collected PDMP request sent returns before clinical eval Analysis PDMP data and clinical data evaluated with respect to rule/algorithm Output Data summary pushed to provider only if triggered and in typical EHR workflow Provider receives alternatives and guidance to discuss results with patient in appropriate context

24 24 Our Research Data Source: Indiana PDMP Payment Type INSPECT* Indiana Board of Pharmacy Oct Mar Prescriptions Currently working with new data: Jan Dec Prescriptions 16% 54% 22% 12M Prescriptions Insurance Medicare Cash Medicaid 67,900 Prescribers 2,100,000 Patients 1,200 Pharmacies * The MITRE Corporation. ALL RIGHTS RESERVED. Approved for Public Release. Distribution Unlimited. Case Number

25 Medication Decision Support Suite, MeDSS - Identifying Potential Abusers at the Point of Care (POC) 25 interviews; gap analysis physicians pharmacists law enforcement federal & state stakeholders descriptive data analytics bivariate statistics clustering graph-analysis transformation geo-temporal predictive data analytics expert annotation machine learning validation UI design web development MeDSS o o o o o actionable web-based easy integration into workflow dynamically generated from agnostic data model quick interpretation of charts and scores 2016 The MITRE Corporation. ALL RIGHTS RESERVED. Approved for Public Release. Distribution Unlimited. Case Number

26 26 Distribution of Doctor Visits Identifying abuse is not as simple as putting a threshold on any single variable 2016 The MITRE Corporation. ALL RIGHTS RESERVED. Approved for Public Release. Distribution Unlimited. Case Number

27 Dimensionality Reduction using Raw Attributes TSNE 1 TSNE 2 2016 The MITRE Corporation.

28 Graph Analysis first order bipartite graph looking at top doctor shoppers 2016 The MITRE

29 Clinically Meaningful Artifacts for Prescriber 29

30 30 Feature Engineering: Inputs for Machine Learning Define new features out of the original raw attributes to maximize discriminating power Identified by domain experts Correlation checks to delete redundant features Test discriminating power Is experimental: cyclical process Challenging to take human-language expression and represent as vectors in Euclidean feature space for inputting to machine learning algorithm 2016 The MITRE Corporation. ALL RIGHTS RESERVED. Approved for Public Release. Distribution Unlimited. Case Number

31 31 Clustering techniques using Features (instead of Raw Attributes) Olap-numRx-CII 3D feature meq feature Graph of distributions for variable: max_days_over_100_meq Number of clusters: 3 Cluster 1 ~ normal(x, , ) Cluster 2 ~ normal(x, , ) Cluster 3 ~ normal(x, , ) Probability density x ( max_days_over_100_meq ) Cluster 1 Cluster 2 Cluster The MITRE Corporation. ALL RIGHTS RESERVED. Approved for Public Release. Distribution Unlimited. Case Number

32 Risk Scoring Patients with Classification Systems Engaged human experts for ground truth : over 2,500 patients annotated by specialists Employed Supervised Machine Learning Models Gains Chart

32 32 Risk Scoring Patients with Classification Systems Engaged human experts for ground truth : over 2,500 patients annotated by specialists Employed Supervised Machine Learning Models Gains Chart Results Multi-class classifier conservative predictions along 2/3 and 3/4 boundaries unsatisfactory mis-classification rate Binary classifier performed well (see gains chart to the left) gain = (accurate prediction /all predictions) decile i 2016 The MITRE Corporation. ALL RIGHTS RESERVED. Approved for Public Release. Distribution Unlimited. Case Number

33 33 MeDSS Patient Timeline and MME Graphs irregular prescribing pattern different attributes but temporally aligned timeline chart MME chart patient s daily MME highlighted when > threshold 2016 The MITRE Corporation. ALL RIGHTS RESERVED. Approved for Public Release. Distribution Unlimited. Case Number

34 34 MeDSS Patient Risk Screen Displays summary statistics - number of Rx, prescribers, pills per day, avg. MME Geo-spatial calculations Payment - number of Rx, prescribers, pills per day, avg. MME Top predictors from machine learning experiments - max_days_over_100meq, trinity_days, early_refills, multiple_drug_days 2016 The MITRE Corporation. ALL RIGHTS RESERVED. Approved for Public Release. Distribution Unlimited. Case Number

35 35 Closing Remarks Recent fines from the Drug Enforcement Administration (DEA) and guidance released by regulatory agencies such as The Joint Commission have tasked health care providers and specifically health systems, to implement controls that will prevent, identify, and respond to drug abuse. PDMPs are an attribute-rich data source. Integrate PDMP with EMR to create a powerful analysis tool o o o non-controlled substance prescriptions and controlled substance prescriptions (current and past) prescriptions from both inpatient setting and outpatient (retail) setting diagnoses codes, medical history Clinical notes including cause of death allow for machine learning software to possibly uncover red flags in patient behavior or history that can lead to addiction or overdose death Cross-walk study using MeDSS with physicians from ED, Family Medicine and Orthopedics. June Oct 2017 at Brigham and Women s Hospital and University of Maryland, Baltimore

36 36 Questions Jaya Tripathi Scott Weiner

6/7/2017. MeDSS: a Data-Driven Tool for Pain Management. Presentation Abstract. The Extent of the Opioid Problem. 200% increase in opioidrelated

6/7/2017. MeDSS: a Data-Driven Tool for Pain Management. Presentation Abstract. The Extent of the Opioid Problem. 200% increase in opioidrelated 1 MeDSS: a Data-Driven Tool for Pain Management Jaya Tripathi Scott Weiner, MD, MPH The International Conference on Opioids, June 11, 2017 2 Presentation Abstract Appropriate pain medication management