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Presentation at UHC  Annual Meeting
 

Presentation at UHC Annual Meeting

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Talk presented at the UHC 2011 Annual Meeting

Talk presented at the UHC 2011 Annual Meeting

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  • Hand off to Andrew at this point.
  • Talk about how the temporal variablesMultiple MI and End Stage Renal helps in generating subsets of data that separate patients with different characteristics.This is Emory specific data set (richer set of variables than current UHC set).
  • Using Multiple MI temporal feature, subset the data and develop a model based on the specific data. Talk about how temporal variable constrained data further helping the predictive model in generating a better list of high risk patients. Overall, we can generate a better final list of high risk patients with the use temporal variables than without.
  • Using Multiple MI temporal feature, subset the data and develop a model based on the specific data. Talk about how temporal variable constrained data further helping the predictive model in generating a better list of high risk patients. Overall, we can generate a better final list of high risk patients with the use temporal variables than without.
  • Statistics about patients who had diagnosis codes related to a disease in the past encounters but no such codes in at least one of the future encounters.UHC dataset 10/1/2006 - 4/30/2011Motivation for this slide: there are lot of encounters with valuable information missing. This information can be captured using temporal/longitudinal variables. Such longitudinal variables improve Predictive Models.
  • Talk about how the temporal variablesMultiple MI and End Stage Renal helps in generating subsets of data that separate patients with different characteristics.
  • Talk about how the temporal variablesMultiple MI and End Stage Renal helps in generating subsets of data that separate patients with different characteristics.

Presentation at UHC  Annual Meeting Presentation at UHC Annual Meeting Presentation Transcript

  • Data Analytics for Readmission:Temporal features, predictivemodeling Joel Saltz, Andrew Post, Doris Gao, Sharath Cholleti, Mark Grand: Emory David Levine, Sam Hohmann: UHC
  • Analytic Information Warehouse Project: Toolsand Analytics to Answer Questions such as:• What fraction of patients with a given category of principal diagnosis will be readmitted within 30 days?• What fraction of patients with a given set of diseases will be readmitted within 30 days?• How does severity and time course of co-morbidities affect readmissions?• How can we best use history of prior hospitalizations to predict readmissions?• What are the medical and socio-economic characteristics of frequently readmitted patients?• Can we translate insight derived from our patient population into rules that can be used to manage patients?
  • Emory Clinical Data Warehouse• EUH, EUHM and WW (inpatient encounters)• Excludes Psych and Rehab encounters• Encounter location (entity, pavilion, unit)• Providers• Discharge disposition• Primary and secondary ICD9 codes• Procedure codes• DRGs• Medication orders• Labs• Vitals• Insurance status• Geographic information
  • Identifying Variables Associated with 30-dayReadmits• Problem: “Raw” variables in the CDW are difficult to use for prediction – Too many diagnosis codes, procedure codes – Continuous variables (e.g., labs) require interpretation – Temporal relationships between variables are implicit• Solution: Transform the data into a much smaller set of variables using heuristic knowledge – Categorize diagnosis and procedure codes using code hierarchies – Classify continuous variables using standard interpretations (e.g., high, normal, low) – Identify temporal patterns (e.g., frequency, duration, sequence) – Apply standard data mining techniques
  • Clinical Data Warehouse/Analytic Information Warehouse (AIW) Cloned periodically Clinical Analytic Data Warehouse Information Derived information Warehouse returned The CDW/AIW Relationship• CDW as source of clinical and administrativedata – cloned periodically (e.g., monthly)• AIW as incubator of algorithms that generatederived information
  • AIW Workflow Cloned periodically Periodic data extraction Analytic Data subset, Multiple Databases Information mapped to a Warehouse standard model Calculation ofMake derivedanalyses variablesavailable (transform)in existingtools Augmented data set Load into multiple output forms
  • Readmissions Analyses (Emory Healthcare)
  • Derived Variables• 30-day readmit• The 9 Emory Enhanced Risk Assessment Tool diagnosis categories• UHC product lines• “Disease indicators” (combinations of diagnosis codes, procedure codes, labs and/or med orders that indicate a condition) – Obesity – Uncontrolled diabetes – End-stage renal disease (ESRD) – Pressure ulcer – Sickle cell disease• Temporal variables derived over multiple encounters – Multiple MI – Multiple past 30-day readmissions – Sickle cell disease – Diabetes/uncontrolled diabetes – CKD/ESRD
  • Emory Enhanced Risk AssessmentTool (ERAT) Diagnoses• Diabetes• Heart Failure• Chronic Kidney Disease• Chronic Obstructive Pulmonary Disease• Acute Myocardial Infarction• Stroke• History of Transplant• Cancer• Pulmonary Hypertension
  • Identifying Variables Associatedwith 30-day Readmits• No variables in the CDW are broadly associated with (or predictive of) readmits across the entire EHC population• Need to drill-down into subpopulations to identify variables that are associated with readmits• Ultimately, may be able to derive subpopulation- specific predictive models of readmissions
  • 3-year+ subset (2008-3/2011) Analytic Information Warehouse
  • Association of CKD with 30-day Readmissions Overall Emory Readmission Rate = 15% CKD?Subsequent 30-day readmit? FALSE TRUE Grand Total30 Day Readmission 19386 7017 26403 Readmission Rate = 21%No 30 Day Readmission 110058 23460 133518Grand Total 129444 30477 159921 ESRD?Subsequent 30-day readmit? FALSE TRUE Grand Total30 Day Readmission 23091 3312 26403 Readmission Rate =27%No 30 Day Readmission 124518 9000 133518Grand Total 147609 12312 159921 Analytic Information Warehouse
  • Association of Multiple MI with 30-day Readmissions Multiple MI?Subsequent 30-day readmit? FALSE TRUE Grand Total30 Day Readmission 685 167 852No 30 Day Readmission 5772 209 5981Grand Total 6457 376 6833 Readmission Rate = 44%
  • Uncontrolled Diabetes (total n=8696, readmit n=1844, Readmit Rate = 21%) Has Pressure Ulcer Pressure ulcer?Subsequent 30-day readmit? FALSE TRUE Grand Total30 Day Readmission 387 128 515 ReadmissionNo 30 Day Readmission 1053 260 1313 Rate = 33%Grand Total 1440 388 1828 Has ESRD ESRD?Subsequent 30-day readmit? FALSE TRUE Grand Total30 Day Readmission 1200 327 1527 ReadmissionNo 30 Day Readmission 3491 712 4203 Rate = 32%Grand Total 4691 1039 5730
  • Sickle Cell Anemia and 30-day Readmits Sickle Cell Anemia Sickle Cell Anemia?Subsequent 30-day readmit? FALSE TRUE Grand Total30 Day Readmission 25905 498 26403 ReadmissionNo 30 Day Readmission 132550 968 133518 Rate = 34%Grand Total 158455 1466 159921 Sickle Cell Crisis SS Crisis?Subsequent 30-day readmit? FALSE TRUE Grand Total30 Day Readmission 25972 431 26403 Readmission Rate = 36%No 30 Day Readmission 132759 759 133518Grand Total 158731 1190 159921
  • Association of MRSA with 30-day Readmissions Overall MRSA?Subsequent 30-day readmit? FALSE TRUE Grand Total30 Day Readmission 25982 421 26403 Readmission Rate = 27%No 30 Day Readmission 132362 1156 133518Grand Total 158344 1577 159921 Stroke MRSA? Subsequent 30-day readmit? FALSE TRUE Grand Total 30 Day Readmission 1203 16 1219 Readmission Rate= No 30 Day Readmission 3996 26 4022 Grand Total 5199 42 5241 38% MI MRSA? Subsequent 30-day readmit? FALSE TRUE Grand Total 30 Day Readmission 836 16 852 No 30 Day Readmission 5942 39 5981 Grand Total 6778 55 6833 Readmission Ra 29%
  • Use of Temporal Variables in creatinguseful subsets of data (5 year dataset) Patient Number of Number of Population Encounters Readmissions Readmission Rate Overall Emory 232645 34270 15% Single MI 17992 2804 16% Multiple MI 1355 492 36% CKD 45664 10818 24% >=4 readmissions 17550 9459 54% Multiple MI and >= 4 readmissions 900 465 52% CKD and >=4 readmissions 6997 3606 52%
  • Predictive Modeling for Readmission• Classify inpatient encounters into high, medium, low risk groups of 30-day readmission based on patients’ characteristics• Data preprocessing and mapping of codes• Predictive modeling – Random forests (ensemble of decision trees) – Ranking of the predictions into high to low risk• Emory specific data sets
  • Random Forests• Random forests: an ensemble of tree predictors• Each tree is created using a random subset of the variables in the dataset• A large number of trees are generated• All of them vote to classify a test example• Reference: Leo Breiman, Random Forests, Machine Learning, 45, 5-32, 2001
  • Random Forest (cont)• Generalization error depends on the strength of individual trees and the correlation between them• Its accuracy is as good as AdaBoost (another robust algorithm)• It is relatively robust to noise and outliers• It gives useful internal estimates of error, correlation, strength and variable importance
  • Variables used in Predictive Modeling• Age, gender, race• Census tract data: population, population by race, average household income, persons per household• Primary and secondary diagnosis codes grouped using ontologies• Lab procedure codes grouped using ontologies• Vitals like heart rate, blood pressure, temperature, respiratory rate, BMI• Medications• Derived variables (next slide)
  • Derived Variables• Disease flags – CKD, MI, HF, COPD, Diabetes, etc.• Medication flags – Diabetes medication count, ACE inhibitor, beta blocker, diuretic, inotropic agent, etc.• Treatment flags – Radiotherapy, chemotherapy• Patient history – Encounter 90 days earlier, 180 day earlier
  • BMI Using WHO Simple Classification (1 year subset 4/2010-3/2011)Percent BMI Category for CKD patients Percent BMI Category for CKD female patients with multiple readmits (n=386) with multiple readmits (n=197) RR=1.2“30 Day Readmission” represents encounters that were followed by a 30 day readmit“No 30 Day Readmission” represents other encounters that were not followed by a 30 day readmit Analytic Information Warehouse
  • Predictive Modeling Results with Temporal Variable Constrained Dataset: MI data (Emory) All MI data and Multiple MI data Predict 30-day ed Risk # of # of Readmission Data encounters Readmissions rate All MI data High 968 360 37% Multiple MI High 68 35 51% All MI data (no predictive modeling) 9674 1648 17% Multiple MI (no predictive modeling) 376 167 44%
  • Predictive Modeling Results with Temporal Variable Constrained Dataset: CKD data (Emory) All CKD data and End Stage Renal CKD Predicted # of # of Readmission Data Risk encounters Readmissions rate CKD High 2284 950 42% End Stage Renal High 952 444 47%All CKD (no predictive modeling) 45664 10818 24% End Stage Renal (no predictive modeling) 3312 12312 27%
  • UHC Data Analyses• Much larger dataset• Much less detailed information about each patient• UHC only has coded data sent by institutions so co- morbidity related ICD-9 codes may be missing• Analyses across patient encounters can pick up chronic co-morbidities that might not be coded in a particular encounter
  • Missing Diagnosis Codes in UHC dataset 10/1/2006 - 4/30/2011Disease Number of Total number Number of Total number Patients with of patients Encounters of encounters missing codes with missing in future codes encountersDiabetes 144806 (8.01%) 1807322 311403 (9.4%) 3300804Heart Failure 197043 (20.1%) 976041 366926 (20.7%) 1765203MI 171213 (21.8%) 784559 301673 (25.8%) 1168056Sickle Cell 2870 (10.5%) 27210 11162 (9.9%) 112268
  • UHCUse of Temporal Variables in Sub setting DataPatient # Total # Readmitted Proportion of PatientsPopulation Encounters Patients ReadmittedMI 310954 47210 15.2%Multiple MI 73227 29017 39.6%Non-ESRD 13023536 1735308 13.3%ESRD 510702 142622 27.9%CKD 1334617 316399 23.7%
  • UHCUse of Temporal Variables in Sub setting DataPatient # Total # Readmitted Proportion of PatientsPopulation Patients Patients ReadmittedDiabetes 2465049 465526 18.8%UncontrolledDiabetes 388417 78005 20.0%ESRD 510702 142622 27.9%UncontrolledDiabetes andESRD 48583 14224 29.8%
  • Readmission Hot Spots
  • UHC “Readmission Hot Spots”1000000 900000 800000 700000 600000 Encounters 500000 Patients 400000 300000 200000 100000 0 1 2 3 4 5 6 7 8
  • Conclusion• Integrative dataset analysis can leverage patient information gathered over many encounters• Temporal analyses can generate derived variables that appear to correlate with readmissions• Hot spots appear to be an important phenomenon and have the potential of leading to patient-level interventions• Predictive modeling has promise of providing decision support• Future analysis will look at temporal patterns of encounters and relationship between LOS and readmission