Homecare Health & Medical Billing Data Science Study

Transcription

1 Combining Traditional Statistical Methods with Data Mining Techniques for Predictive Modeling of Homecare Outcomes Bonnie L. Westra, PhD, RN, Assistant Professor University of Minnesota, School of Nursing 1

2 Acknowledgments Co-Investigators Kay Savik, MS Cristina Oancea, MS John H. Holmes, PhD Lynn Choromanski, MS Karen Dorman Marek, PhD, MBA, RN, FAAN Industrial Partners CareFacts Information Systems CHAMP Software Funding University of Minnesota Digital Technology Initiative Grant

3 Problem Increasing homecare/ community-based care Annual expenditure in 2005 of $47.5 billion 2000 CMS implemented PPS for Medicare patients Concern about decrease in service/ visits on outcomes 28% hospitalization rate nationally remained constant Limited research on ways to reduce hospitalization

4 Research Aims The purpose of this study was to develop predictive models for risk factors associated with increased likelihood of hospitalization of homecare patients and discover if interventions documented as part of routine care using the Omaha System influence hospitalization. Use knowledge discovery in databases combined with traditional statistics. Reported here is the first models using traditional statistics.

5 Design/ Sample Secondary analysis of EHR data OASIS and Omaha System interventions from two different EHR systems and 15 homecare agencies. Data included All open charts in 2004 for patients with a minimum of two OASIS records for the start and end of an episode of care and who also had Omaha System interventions.

6 OASIS Data

7 Omaha System Interventions Intervention = Problem + Category + Target 44 Problems Environmental, Psychosocial, Physiological, Other Health Related problems Interventions Category & Target 4 Categories Monitoring (Surveillance) Coordinating (Case Management) Providing Care (Treatments & Procedures) Teaching (Teaching, Guidance, & Counseling) 63 Targets i.e. exercise, coping, cardiac care

8 Data Preparation/ Transformation Data preparation Frequencies, descriptive statistics, and histograms to assess for missing values, duplicate records, and out of range values Data transformation Episodes of care unit of analysis Summative scales - Prognosis, Pain, Pressure Ulcers, Stasis Ulcers, Surgical Wounds, Respiratory Status, ADLs, IADLs Clinical Classification Software - primary diagnoses and then reduced into 51 smaller groups within 11 major categories Charlson Index of Comorbidity - additional medical diagnoses Interventions theoretically grouped into 23 categories Created dummy variables for non-normally distributed data

9 Data Analysis Latent class analysis ADL Scale (M0640 M0710) Who Provides Assistance (M0350) Management of medications (M0780) Diagnosis group (M0230 CCS Groups) Logistic regression Create models for predictors of hospitalization - OASIS Added interventions Omaha System Interventions

10 Demographics 2,806 patients Mean age 74.4 (SD = 14.1) 64.6% Females 97.9% White 4,242 Episodes Length of stay ranged from 1-6,354 days (Median = 38 days) 48.8% discharged 38.6% transfer to inpatient setting 1,620 (38.4%) hospitalized 29.9% continued with care 1.7% died

11 Demographics Primary diagnoses (most frequent) 18.8% 18.1% 9.1% 7.3% 2.3% cardiac and circulatory diseases orthopedic/ trauma surgery and follow up endocrine and nutrition respiratory problems infectious diseases Charlson Index of Comorbidity 0 10 with a mean of.58 (SD = 1.32) Interventions (384,081) 62.5% 44.9% 30.2% 16.0% monitoring teaching treatments case management

12 Class: Description Class 1: Functionally Impaired Latent Classes Class 2: Orthopedic/ Trauma Surgery Follow Up Class 3: Cardiac, circulatory Class 4: Mixed Diseases Number (%) episodes 622 (10%) 1,381 (23%) 1,188 (20%) 2,843 (47%) Hospitalized, % 45% 11% 32% 26% ADL score 19.5 (16.3) Assistance - resident in home Medication management - needs administration Primary diagnoses 76% 74% Mixed Endocrine/ nutrition 21% CNS/vision/ hearing 28% Cardiac/ circulation 100% Orthopedic/ trauma surgery and follow up 100%

13 Class I: Functionally Impaired Risk Factors Only Risk Factors After Interventions Variables OR (95%CI) p-value OR (95%CI) p-value Assistance with IADLs.20 (.04, 1.05) (.03,.81).03 Prognosis - Poor 1.9 (1.3, 2.9) (1.5, 3.3) <.001 Charlson Index Moderate risk Medicare as homecare payor 2.6 (1.2, 5.4) (1.6, 6.9) (1.1, 3.7) (1.3, 4.1).003 Significant Interventions Variable Frequency OR(95%CI) p-value Monitoring Injury Prevention Low 1.7 (1.1, 2.8).03

14 Class II: Orthopedic/ Trauma Surgery and Follow Up Risk Factors Only Risk Factors After Interventions Variable OR(95%CI) p-value OR(95%CI) p-value Medicare as homecare payor.51 (.25, 1.0) (.33,.90).02 Charlson Index Very low risk 2.0 (1.1, 3.7) (1.6, 3.9) <.001 Charlson Index Low risk 2.5 (1.0,6.3) (.99, 4.2).05 Charlson Index Moderate risk 1.9 (.41, 9.0) , 8.0).05 Prognosis - poor 2.0 (1.2, 3.4) (1.3, 2.6) <.001 Prognosis - fair 2.5 (.91, 7.0) (1.3, 6.2).008 Pain Scale: Moderate 2.4 (1.2, 4.8) (.91, 2.7).11 Pain Scale: Moderately Severe 2.4 (1.5, 7.9) (1.6, 5.9) <.001 Pain Scale: Mild 2.9 (1.3, 6.4) (.78, 2.9).22 Patient equipment: Some assistance required 3.2 (1.1, 9.4) (1.5, 7.8).003 Significant Interventions Variable Frequency OR(95%CI) p-value Providing Injury Prevention Treatment Moderate.36 (.14,.91).03 Monitoring Injury Prevention Moderate 2.0 (1.0, 3.7).04 Coordinating Supplies & Equipment Low 3.4 (1.6, 7.0).001

15 Class III: Cardiac or Circulatory Diseases Risk Factors Only Risk Factors After Interventions OR (95%CI) p-value OR (95%CI) p-value IADL Status: Moderately Dependent 1.6 (1.1, 2.3) (.81, 1.4).62 IADL Status: Dependent 2.3 (1.4, 3.7) (.84, 2.0).23 Expected Prognosis: Fair 1.8 (1.1, 2.8) (1.4, 2.7) <.001 Expected Prognosis: Poor 1.6 (1.1, 2.4) (1.1, 2.0).006 Pain Scale: Mild 1.9 (1.2, 3.0) (1.2, 2.4).006 Pain Scale: Moderate 2.2 (1.5, 3.1) < (1.3, 2.4) <.001 Pain Scale: Severe 1.7 (.79, 3.7) ( ).03 Charlson Index Very Low risk 2.1 (1.4, 3.1) < (1.3, 2.4) <.001 Charlson Index Low risk 2.1 (1.4, 3.4) < (1.4, 2.9) <.001 Charlson Index Moderate risk 2.6 (.81, 8.3) (1.1, 5.9).03 Bowel incontinence > 1 time per week or patient has an ostomy 2.3 (.83, 6.5) (1.0, 4.0).04 Patient equipment: Needs setup 3.9 (1.9, 7.8) < (1.2, 3.9).01 Significant Interventions Variable Frequency OR(95%CI) p-value Teaching Disease Treatment Moderate.50(.26,.94).03 Providing Medication Treatment Low 1.9(1.1, 3.1).01 Teaching Disease Treatment High 3.0(1.2, 7.5).02

16 Class IV: Mixed Diseases Risk Factors Only Risk Factors After Interventions Variable OR (95%CI) p-value OR (95%CI) p-value Medicare payor for homecare.35 (.26,.47) < (.28,.52) <.001 Other insurance for homecare.39 (.24,.63) < (.26,.67) <.001 Bowel Incontinence < 1 time per week.43 (.21,.89) (.19,.86).02 Surgical Wounds: Moderate.62 (.43,.88) (.43,.88).008 Expected Prognosis: Poor 1.4 (1.1, 1.7) (1.1, 1.8).005 Expected Prognosis: Fair 2.1 (1.5, 2.8) < (1.4, 2.7) <.001 Vision: partially impaired 1.4 (1.0, 2.1) (.98, 2.0).06 Vision: severely impaired 3.5 (1.7, 7.0) < (1.8, 8.8) <.001 IADL: moderately dependent 1.5 (1.1, 2.0) (1.1, 2.1).003 IADL: dependent 1.8 (1.2, 2.5) (1.3, 2.7) <.001 Patient equipment: Needs Setup 1.6 (1.0, 2.6) (1.1, 2.7).03 Patient equipment: Some assistance required 2.0 (1.2, 3.3) (1.3, 3.5).003 Charlson Index Very Low risk 2.5 (1.9, 3.4) < (1.8, 3.2) <.001 Charlson Index Low risk 2.5 (1.9, 3.3) < (1.7, 3.0) <.001 Charlson Index Moderate risk 3.4 (2.0, 5.7) < (1.7, 4.8) <.001

17 Class IV: Mixed Diseases Significant Interventions Variable Frequency OR (95%CI) p-value Coordinating Supplies & Equipment High.16 (.03,.74).02 Teaching Medications High.39 (.17,.88).02 Providing Injury Prevention Treatment High.43 (.19,.97).04 Providing Injury Prevention Treatment Moderate.44 (.27,.71) <.001 Monitoring Medications Moderate.58 (.36,.96).03

18 Discussion Homecare patients are heterogeneous in needs latent class analysis was useful ADLs, management or oral medications, caregiver assistance, and primary diagnoses Differences between classes Similarities across classes Most consistent predictors of hospitalization are Charlson Index of Comorbidity, prognosis, Medicare, patient management of equipment, and IADLs The addition of interventions to the predictive models for hospitalization modified some predictors - Injury prevention Some interventions were risk factors, others were protective

19 Limitations First study is hypotheses generating, not testing Transformation of data Considerable missing data Heterogeneity of data Time span Payors Problems/ needs Interventions Traditional statistical methods

20 Future Research KDD to compare models Testing of transformations Limit episodes Complete data Time span Problem focus Comparing KDD and traditional statistics for predictive modeling with additional outcomes Work with agencies to standardize interventions