Population Grouping: The Canadian Experience

Transcription

1 Population Grouping: The Canadian Experience PCSI, The Hague October 16, 2015 Douglas Yeo Holly Homan Victoria Zhu Craig Homan 1

2 Canadian Population Grouping Methodology The Journey so Far Health Condition Categories Cost Weights Mutually Exclusive Classification 2

3 The Journey So Far Douglas Yeo 3

4 The Journey So Far What is the POP Grouping Methodology Similar concept to CIHI s other case mix methodologies Some differences from CIHI s other case mix methodologies Does not focus on any one sector or setting Target population includes all persons registered for publicly-funded health care Looks at person over an extensive time period Two major components of the methodology Clinical classification Predictive indicators 4

5 What are the Timelines for the Population Grouping Methodology Project? September 2014 Interim release Mapping tables of ICD codes to health conditions April 2015 Alpha version Clinical classification: health condition flags Predictive indicators for health resources Grouping software October 2015 Beta version Functional status classification, for continuing care sub-population Socioeconomic effects incorporated into indicators March 2016 version 1.0 Mutually exclusive classification Alpha and beta for expert group; 1.0 for public release 5

6 Some Population Grouping Methodology Applications Profiling and planning Coordinated care and targeted care management High users Risk adjustment Health system indicators Funding Physician Regions 6

7 Concurrent and Prospective Periods Clinical classification Concurrent period clinical data used to build clinical profiles Predictive indicators (e.g. cost, mortality) Predicted cost during concurrent period Predicted cost during prospective period 7

8 Health Condition Categories Holly Homan 8

9 Evaluation Criteria Criteria Types of Questions Clinical Relevancy Are the disease categories useful to clinicians? Is it useful for managers and administrators in describing populations? Explanation of Variation How well do the categories differentiate costs? How well do the categories differentiate utilization needs? What is the R 2? Logical Hierarchy / Transparency Manageable Number of Groups Does the methodology apply hierarchies where appropriate? Is there a large number of terminal categories? Do certain categories not contribute to identifying chronic population? Sensitivity to Data Inputs Does the methodology address variations that might exist in data inputs? Reliability of Predictive Indicators Does the methodology yield similar results to population estimates? What is the stability of indicators over time? 9

10 Health Condition Categories Started with medical Case Mix Groups (CMG+) Vetted through a clinical panel Modified to fit a population methodology Input from expert advisory group and physician panel Malignancy teased out Physician claims data Diagnostic, supportive, ongoing 10

11 Assigning Diagnoses to a Person CIHI source data Hospital inpatient Day surgery Physician claims CCRS assessments Diagnosis codes ICD-10-CA ICD-9 G81.99 Hemiplegia unspecified S Fx lumbar vertebra J20.9 Acute bronchitis, unspecified U Snowboarding Z13.5 Special screening eye & ear Martin 11

12 Assigning Health Condition Categories Diagnosis codes Apply to persons Look at all encounters over the concurrent period Certain POP health (e.g. 2 years) conditions tagged to a Map each diagnosis to person one of the 214 health conditions A07 Paralytic Syndrome / Spinal Cord Injury H42 Fracture / Dislocation Vertebrae, Pelvis D44 Acute & Other Respiratory Diseases Martin 12

13 Health Condition: Tagging Rules Tagging Rules > 1 time rule for hospital stays >Physician claims data 1 time and 2 times rule A07 Paralytic Syndrome / Spinal Cord Injury H42 Fracture / Dislocation Vertebrae, Pelvis D44 Acute & Other Respiratory Diseases Martin 13

14 Health Condition: Clinical Overrides Tagging Rules > 1 time rule for hospital stays >Physician claims data 1 time and 2 times rule Clinical Overrides Health Condition overrides address redundancies that may exist on a profile after the assignment of health conditions is completed A07 Paralytic Syndrome/Spinal Cord Injury H42 Fracture/Dislocation Vertebrae, Pelvis D44 Acute & Other Respiratory Disease Martin 14

15 Number of Conditions Present During the Concurrent Period, by Age Group, Alberta 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 5+ Conditions 4 Conditions 3 Conditions 2 Conditions 1 Condition 0 Conditions Non-Users Age Group (in Years) 15

16 Cost Weights Victoria Zhu 16

17 Assigning Cost Weights to Persons (Illustration) Q04 Depression Q07 - Drug/Alcohol Abuse/Dependence Cost Weights (hospital + physician costs) Cost Indicator Effect Concurrent Prospective Q Q Total Joe Joe s concurrent cost weight is 1.24 Joe s prospective cost weight is

18 Modeled Cost for a Person (Illustration) Joe s concurrent cost weight is 1.24 Joe s prospective cost weight is 1.38 Q04 Depression Q07 - Drug/Alcohol Abuse/Dependence Population average concurrent cost is $3,258 Population average prospective cost is $1,752 Joe Joe s expected concurrent cost: $4,040 = $3,258 x 1.24 Joe s expected prospective cost: $2,418= $1,752 x 1.38 Mock data 18

19 Population distribution Populations Non Users Users W/O HCs Users With HCs Total Population Volume 444, ,894 3,264,548 3,921,754 * and Alberta data used in methodology development 19

20 Regression Models Additive models with linear regression Cost as the response variable Separate models for concurrent and prospective Separate models applied to each sub-population Non-users: OLS, Age/Sex Users without any health conditions: OLS, Age/Sex Users with at least one health conditions: WLS, 214 Health Conditions 20

21 Goodness of Fit (Concurrent) Concurrent Models Goodness of Fit Model Partition Vol Avg Cost ($) Avg Predicted Cost ($) Bias ($) Prediction Error R 2 (%) Non Users Users with No Health Conditions Users with Health Conditions Overall Est 310, Val 133, Est 149, Val 63, Est 2,284,872 3,897 3, , Val 979,676 3,896 3, , Est 2,744,652 3,259 3, , Val 1,177,102 3,257 3, , * and Alberta data used in methodology development 21

22 Goodness of Fit (Prospective) Prospective Models Goodness of Fit Model Partition Vol Avg Cost ($) Avg Predicted Cost ($) Bias ($) Prediction Error R 2 (%) Non Users Users with No Health Conditions Users with Health Conditions Overall Est 310, Val 133, Est 149, Val 63, Est 2,272,722 2,029 2, , Val 972,655 2,032 2, , Est 2,732,444 1,751 1, , Val 1,169,755 1,751 1, , * and Alberta data used in methodology development 22

23 Profiling of Population (Concurrent) Decile sss Volume Average Actual Cost Average Modeled Cost Proportion of Total Costs Average Number of Health Conditions Average Age 1 392, % , % , % , % , % ,175 1,101 1, % ,175 1,623 2,096 5% ,176 2,573 3,380 8% ,175 4,453 5,720 14% ,176 20,952 17,966 64% Overall 3,921,754 3,258 3, % * and Alberta data used in methodology development 23

24 Profiling of Population (Prospective) Decile Volume Average Actual Cost Average Modeled Cost Proportion of Total Costs Average Number of Health Conditions Average Age 1 390, % , % , % , % , % , % ,220 1,290 1, % ,220 1,754 1, % ,220 2,647 3, % ,220 8,204 8, % Overall 3,902,199 1,751 1, % * and Alberta data used in methodology development 24

25 What s New in Beta Health Conditions 225 health conditions instead of 214 Clinical overrides applied Any changes in classification are applied Health Condition Interactions OLS used for users with health conditions November 2015 release 25

26 Plan for Version 1.0 & Future Release Reflects Mutually Exclusive Rule More Predictors Functional Status SES etc. More Predictive Indicators Version 1.0 release: March

27 Questions? Comments? Thank you! 27

28 Mutually Exclusive Classification Craig Homan 28

29 Identification of Conditions Additive methodology originally contained 214 conditions Maintain same tagging rules as additive Every evolution (SES, over-ride rules, functional status) in additive model ripples through this model Mutually Exclusive methodology Rolled 214 conditions up to 111 conditions Similar cost and clinical characteristics e.g. Merged CAD/Arrhythmia/Other Heart Disease Clinically validated Create room for splits while maintaining clinical meaningfulness Age/gender tested and not implemented 29

30 Categories Each condition linked to a Category Major Moderate Minor Other Chronic Cancer Newborn Acute Mental Health Chronic Acute Chronic Acute Cancer Mental Health Obstetrics 30

31 Hierarchy in Mutually Exclusive Methodology Hierarchy identifies single most significant condition for each person Categorized to the highest condition of the 111 on the list Cost, clinical considerations Concurrent and prospective costs Cell # Description Hierarchy Category S01 Palliative State (Acute) 1 Major Acute S41 Transplant Complication 2 Major Acute S43 Ostomy Complication 3 Major Acute N41 Extremely Low Birth Weight or Immaturity 4 Major Newborn D41 Respiratory Failure 5 Major Acute D04 Pulmonary Hypertension 6 Major Chronic A07 Paralytic Syndrome/Spinal Cord Injury 7 Major Chronic JT1A Diabetes/Hypoglycemia with Chronic Kidney Disease/Failure 8 Major Chronic J01 Cystic Fibrosis 9 Major Chronic I05 Skin Ulcer (incl. Decubitus) 10 Major Chronic 31

32 Effect of Comorbidities Identified significant comorbidities Major/moderate Categories Split where sufficient volume and cost distinctions seen No cost difference where inside/outside same body system 32

33 177 Cell Methodology Significant comorbidity splits Category Rank 111 Cond. 177 cells Description 177 Cell Logic Major Chronic 6 D04 D04 Pulmonary Hypertension Major Chronic 6 D04 D04_mm Pulmonary Hypertension, with major/moderate comorbidity Major Chronic 7 A07 A07 Paralytic Syndrome/Spinal Cord Injury Major Chronic 7 A07 A07_mm Paralytic Syndrome/Spinal Cord Injury, with major/moderate comorbidity Major Chronic 9 J01 J01 Cystic Fibrosis Major Chronic 9 J01 J01_mm Cystic Fibrosis, with major/moderate comorbidity Major Chronic 15 E01 E01 Heart Failure Major Chronic 15 E01 E01_mm Heart Failure, with major/moderate comorbidity 33

34 443 Cell Methodology Built on 177 cells Added total condition count splits where appropriate Volume, cost distinction Category Rank 111 Cond. 177 cells Description 177 Cell Logic Total Condition Count Splits 443 cells Major Chronic 6 D04 D04 Pulmonary Hypertension no split Major Chronic 6 D04 D04_mm Pulmonary Hypertension, with major/moderate comorbidity 2-4,5-10,11-14,15+ Major Chronic 9 J01 J01 Cystic Fibrosis no split Major Chronic 9 J01 J01_mm Cystic Fibrosis, with major/moderate comorbidity no split Major Chronic 15 E01 E01 Heart Failure 1-2,3-4,5+ Major Chronic 15 E01 E01_mm Heart Failure, with major/moderate comorbidity 2-3,4-6,7-9,10-14,15+ 34

35 689 Cell Methodology Rolled back to 111 Conditions Added comorbidity splits where appropriate Major/moderate/minor comorbidities, Total Condition Count A09 Moderate Chronic Epilepsy no moderate, no minor comorbidities A09a Moderate Chronic Epilepsy no moderate, with <3 Minor Comorbidities and Total Condition Count =2 A09b Moderate Chronic Epilepsy no moderate, with <3 Minor Comorbidities and Total Condition Count 3+ A09c Moderate Chronic Epilepsy no moderate, with 3+ Minor Comorbidities A09d Moderate Chronic Epilepsy with Moderate Comorbidity and < 2 Minor Comorbidities A09d Moderate Chronic Epilepsy with Moderate Comorbidity and 2+ Minor Comorbidities 35

36 Overview of Model Evolution and R 2 Concurrent Data Prospective Data Development 214 Cond. 111 Cond. 177 Cells 443 Cells 689 Cells Estimation (70%) Validation (30%) Estimation (70%) Validation (30%) Additive model Increased number of cells for greater clinical distinction Added Major/ Moderate splits where applicable Added cellspecific count splits where possible Comorbidity and condition count splits 36

37 Next steps Chronic vs acute Cost not different Clinical perspective Socioeconomic status (SES) (R 2 +4%) Functional Status Compare goodness of fit statistics for all models Additive and mutually exclusive quite similar Provide recommendations for further development 37

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