Predicting 30-day Readmissions is THRILing
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1 2016 CLINICAL INFORMATICS SYMPOSIUM - CONNECTING CARE THROUGH TECHNOLOGY - Predicting 30-day Readmissions is THRILing OUT OF AN OLD MODEL COMES A NEW
2 Texas Health Resources 25 hospitals in North Texas 14 wholly owned hospitals 133,903 Inpatient Visits 1,238,392 Outpatient Encounters 469,309 ED Visits 89,452 Surgeries 27,200 Deliveries 5,500 Active Physicians 7,500 RN s 22,000 Employees
3 Texas Health Resources - Organizational Background Texas health resources is one of the largest faith-based, nonprofit health care delivery systems in the united states and the largest in north Texas in terms of patients served. The system's primary service area consists of 16 counties in north central Texas, home to more than 6.8 million people. 3
4 Texas Health Resources 25 hospitals in North Texas 14 wholly owned hospitals 133,903 Inpatient Visits 1,238,392 Outpatient Encounters 469,309 ED Visits 89,452 Surgeries 27,200 Deliveries 5,500 Active Physicians 7,500 RN s 22,000 Employees
5 LEARNING OBJECTIVES Understand the reporting requirements and exclusions related to 30- day readmissions Identify the elements of LACE+, and the issues related to the tool in today's patient populations Learn how THR prioritized readmission risk elements, both existing in LACE+ and new additions to make the project manageable for a small team Determine how the organization identified statistically significant patient characteristics that contribute to 30-day readmissions
6 Business Model Expense reduction Revenue Decrease unit cost Decrease utilization Growth Delivery Efficiency (service/care) All care team members practicing at the top of their license Streamlined work flow Process automation Decrease care process variation Appropriate Utilization (level/type) Population health risk management strategies Care coordination and navigation Decrease variation in diagnosis and treatment Increase Membership Total population risk and global budget arrangements Bundle services and payment for episodes of care or chronic health conditions while increasing quality and member experience 6
7 Texas Health Resources & Readmission Risks Used brand name readmission risk indicators for 3 years Not effective/efficient enough in targeted outreach Some tools proprietary and risk factors were unknown Gap in managing and reducing readmissions Limited resources Can t reach every patient but need to reach the right patients Requested for more data that defined our unique population Formation of a Readmission Taskforce
8 Where Are We Going?
9 LACE+: Part 1 (van Walraven, Wong, & Forster, 2012) (Quan, et al., 2005)
10 LACE+: Part 2 (van Walraven, Wong, & Forster, 2012) (Quan, et al., 2005)
11 How LACE+ Score is Determined ALC status CMG Score Teaching hospital Sex Length of stay Age LACE+ Score Low Risk ED visits & Elective hospitalizations Admission urgency Sum of weighted scoring of diagnoses Count of urgent admissions (last 365 days) Medium Risk High Risk Sum of weighted scores for individual characteristics Multifactorial Index Calculation score (Charlson Index) (van Walraven, Wong, & Forster, 2012) (Quan, et al., 2005)
12 Challenges with LACE+ Variables in the LACE+ algorithm aren t in EHR: Case-mix group (CMG) score reduces c-statistic (0.753 vs ) (van Walraven, Wong, & Forster, 2012) Alternate Level of Care (ALC) Status Disease Conditions: Individual ICD codes Difficult to interpret and maintain Documentation inconsistency (Problem List vs. Patient History) Risk stratification: Too many high risk patients who did not readmit Resources limited to address all high risk Urgent admission source of truth Some high utilizers scored as a low risk
13 Guide to Classification of C-Statistic (ROC) = excellent (A) = good (B) = fair (C) = poor (D) = fail (F) (Tape, n.d.)
14 THR Goal Create a predictive scoring tool: Tailored to THR s specific patient populations Variable must be available in EHR prior to discharge Decrease the amount of patients designated as High Risk while improving the accuracy of High Risk designation Manageable workload for intervention Trustworthiness of the designation C-stat goal of 0.78 to Elevate from a fair tool to a good tool
15 Formation of Innovation Group CLINICAL INFLUENCE Membership Two physician champions Population Health Care Transition Managers Nursing Focus Concept development Version review and approval, ensuring tool fits into provider workflows Development of interventions TECHNICAL EXPERTISE Membership Clinical & Nursing Informaticists EHR builder Focus Feasibility Maintainability Replicability in EHR
16 Texas Health Readmission Indicator List (THRIL) Systematic approach to development 4 versions with incrementally increasing levels of depth 3 months for analysis and design 2 months for EHR build, testing, change management process
17 Technical Requirements Analytics Tools: SAS EG & SPSS Statistics CLINICAL INFORMATICIST Created test environment for algorithm changes Projected and actual statistical significance Data mining Variable weighting Data validation NURSE INFORMATICIST Identification of source of truth Documentation reliability Data mining & Dataset preparation Determining clinical relevance of variables Evaluation of variables and readmission risk Variable weighting/scoring Build and testing in EHR Training and implementation
18 THRIL Version Analysis Example Systematic analysis Incremental change Careful evaluation of impact
19 From LACE+ to THRILv1 Texas Health Readmission Indicator List (THRIL) Version 1 Addressed source of truth issues Reweighted disease conditions Utilized patient history documentation in addition to Problem List Added new conditions (sepsis, antepartum complications, pneumonia) Restratified risk categories Adjusted age ranges, admission counts, point assignments Added raw counts of ED utilization and hospital admissions to target high utilizers
20 THRIL v1 (part 1) Predictor Sex Male Female Urgent Admission Discharge Institution Teaching or small institution Large non-teaching Length of Stay (days) < >10 Number of ED Visits Last 6 Months Number of elective admission (last 365 days) 0 >0 Raw number of all inpatient admissions (last 365 days) Disease Conditions Score (based on age, # Points (ED acuity) Raw number 0 6 # (see next page) 20
21 THRIL v1 (part 2) Disease Conditions & Point Value (Sum total points) 1 point 2 points 3 points Peripheral Vascular Disease (current or history) Cerebrovascular Disease (current or history) Dementia (current or history) Connective tissue disease (current or history) Ulcer disease (current or history) Mild liver disease (current or history) Hemiplegia/paraplegia (current or history) AIDS (current or history) Hypertension (current or history) Myocardial Infarction (current) Diabetes w/o complications (current or history) Tumor/Cancer/Leukemia/Lymphoma (current) THRIL Disease Scoring Index Congestive Heart Failure (current or history) Chronic Pulmonary Disease (current or history) Moderate to severe renal disease (current or history) Diabetes w/complications (current or history) Moderate to severe liver disease (current or history) Sepsis (current or history) Antepartum complications (current) Pneumonia (current) Metastatic tumor (current) Previous admissions 2 or less (including current admission) Previous admissions 3 or more (including current admission) Age (years) Point Value 1 Point Value = 2-3 Point Value >3 Point Value 1 Point Value = 2-3 Point Value >3 < >
22 THRIL v1 Risk Stratification Low risk = 28 Medium risk = Medium-High risk = High risk = 81
23 Count of Patients Categorized as High Risk Readmission Rate for High Risk Patients LACE+ THRIL v1 THRIL v2 Projected % 22.02% 21.70% 22.96% 17.48% 29.23% % 30.56% % 39.41% Readmitted Not Readmitted Jan-16 Feb-16 Mar-16 Apr-16 May-16 June June Jul-16 Aug The height of each bar represents the total number of patients categorized as High Risk for readmission. Aug The percentage displayed above each bar is the readmission rate for the High Risk patient population. Higher percentages are better, meaning we are identifying more readmitters in the High Risk bucket. 28.5% increase readmissions to the high risk bucket.
24 C-stat Score Comparison of LACE+ to THRILv Chart Title February March April May June 1-7 June July August LACE THRIL v1 production The graph displays the statistical c-stat score for each month. A c-stat is a statistical calculation that determines the predictive power of the LACE+/THRIL v1 score (A higher c-stat score is better). The red line represents the current LACE+ c-stat scores from February June 7, As you can see, the c-stat scores levelled off around Good news: The THRIL v1 score that went into production on June 8 has the highest c-stat to date of.780. This was even slightly better than our projected THRIL v1 test c-stat scores. 24
25 From THRILv1 to THRIL v2 Highest areas of impact Medical History list count Surgical History list count Allergy list count Schedule I & II allergy count Braden Score <19 at discharge Existence of a Pressure Ulcer How many times a pain score of 10 is reported Isolation status
26 Case Management Interventions Low risk = 28 DC Education begins on day of admission; meds reconciled; follow-up appointment made by the CNL. Medium risk = DC Education begins on day of admission; find a PCP if necessary; CTM makes follow-up appointment; Meds reconciled; community resources as indicated Medium-High risk = DC education begins on day of admission; CTM arranges home health, rehab, skilled care based on criteria and patient acuity. Refer to Transition Housecalls if possible. High risk = 81 Complex case management; assessment for advance directives, end of life planning, palliative care / hospice appropriateness
27 Lessons Learned Research, front-line providers, and organizational leaders all impact analytic tools Leverage existing providers and technology to develop foundational tools to develop reliable baseline processes The making of a predictive tool is not a short-term project Allow for ample time to test and adjust scores and weights Avoid scope-creep Study the marketplace for areas to study Be patient
28 References Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J.,... Ghali, W. (2005). Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care, 43(11), Tape, T. (n.d.). The Area Under an ROC Curve. Retrieved from Interpreting Diagnostic Tests: van Walraven, C., Wong, J., & Forster, A. (2012). LACE+ index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data. Open Med, 6(3), e80- e90. Retrieved September 2016, from
29 PRESENTER CONTACT INFORMATION
30 QUESTIONS & DISCUSSION
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