Leveraging the HSX Clinical Data Repository to Improve Behavioral Health Care:

Transcription

1 Leveraging the HSX Clinical Data Repository to Improve Behavioral Health Care: Insights from the Montgomery County Hospital Partnership HEALTHSHARE EXCHANGE BOARD OF TRUSTEES ANNUAL MEETING NOVEMBER 14,

2 Montgomery County Hospital Partnership Convened by HealthSpark Founda6on, facilitated by Health Care Improvement Founda6on since November 2015 In context of ACA requirements for hospitals, goal is to collaborate on a par6cular unmet community need: Each hospital, in partnership with county health department and other stakeholders, priori%ze %me and resources to iden%fy and implement las%ng solu%ons to address a pressing community health need in a coordinated manner 2

3 Acute Care Hospital ParPcipants Abington Jefferson Health* Bryn Mawr Hospital* Einstein Medical Center Montgomery* Grand View Hospital Holy Redeemer Health System* Lankenau Medical Center* Lansdale Hospital* PoSstown Hospital Suburban Community Hospital * Included in dataset from HSX 3

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5 Partnership Behavioral Health Aims INDIVIDUAL: Improved health outcomes for individuals with behavioral health needs in Montgomery County SYSTEM: Increased system capacity in Montgomery County to provide high quality, integrated care, characterized by 6mely access to care/services that are person-centered, cost-effec6ve, and appropriate ( right care at right 6me ) COMMUNITY: Decreased s6gma and greater support in the community, increased general community mental wellness in Montgomery County 5

6 Data Analysis Project Goal of retrospec6ve data analysis: Gain understanding of demographic paserns of emergency department (ED) u6liza6on by pa6ents with behavioral health needs in the county Results to be used to design and target interven%ons to increase the capability of hospital EDs in the county to effec6vely and safely address pa6ents behavioral health needs 6

7 Approach Data source: HealthShare Exchange Clinical Data Repository Data drawn from member hospitals Admission, Discharge, Transfer (ADT) feeds Pull CY first 6 months of CY2017 ED encounter data across the 6 member hospitals also par6cipa6ng in the Partnership Supplemental analyses for hospitals not currently members of HealthShare Exchange (HSX) 7

8 Ensuring Data Security HCIF executed business associate agreements with HSX and each par6cipa6ng hospital HCIF HIPAA data security program established Access to data and analysis took place in secure HSX server environment 8

9 Analysis Variables U%liza%on & Prevalence Emergency department (ED) encounters With mental health (MH) diagnosis With substance use disorder (SUD) diagnosis With co-occurring (MH & SUD) disorders With co-morbid (MH or SUD + physical) condi6ons Top diagnoses/condi6ons (3 for mental health, 3 for substance use) Pa%ent Characteris%cs Sex (Female, Male) Race (AI/AN, Asian, Black, NH/OPI, White) Ethnicity (Hispanic, Non-Hispanic) Age, in years (<12, 12-17, 18-25, 26-44, 45-64, 65+) Zip codes, categorized as below Montgomery County (with further breakdown: Abington, Lansdale, Lower Merion, Norristown, PoRstown, Telford Souderton) Philadelphia County Other/Unknown 9

10 Analysis Details Samples Aggregate (all hospitals) Pa6ents with mul6ple encounters/seen at mul6ple sites Hospital-specific Level Encounter Pa6ent Frequencies Overall u6liza6on and prevalence U6liza6on and prevalence x pa6ent characteris6cs Addi%onal Notes Inferen6al sta6s6cs not calculated due to data quality issues Cells with low numbers (<11) suppressed; bosom two cells suppressed to prevent back-calcula6on 10

11 Constraints/LimitaPons of the Data Inconsistent coding of race, ethnicity, and ICD-10 codes No discharge 6me to calculate length of stay Disposi6on data not available Unable to know primary vs. secondary diagnosis Unable to calculate severity due to missing diagnosis data Missing or non-specific payer data 18-month period complicates encounter month analysis 11

12 Select Analyses FEATURING FOUR FACILITIES 12

13 PaPents Total # of pa%ents (Jan 1, 2016 Jun 30, 2017) Total # of pa%ents with behavioral health (BH) diagnosis Total # of pa%ents with mental health (MH) diagnosis Total # of pa%ents with substance use disorder (SUD) diagnosis Total # of pa%ents with co-occurring disorders Total # of pa%ents with comorbid condi6ons 367,637 24,716 (7% of all pa*ents) 18,440 (5% of all pa*ents) 8,240 (2% of all pa*ents) 1,964 (1% of all pa*ents) 20,885 (6% of all pa*ents) 13

14 PaPent CharacterisPcs: Sex Behavioral Health Diagnosis Mental Health Diagnosis Substance Use Disorder Diagnosis Co-Occurring Disorders Comorbid Condi%ons Sex N % N % N % N % N % Male % % % % % Female % % % % % TOTAL 24,716 18,440 8,240 1,964 20,885 14

15 PaPent CharacterisPcs: Age Behavioral Health Diagnosis Mental Health Diagnosis Substance Use Disorder Diagnosis Co-Occurring Disorders Comorbid Condi%ons Age Categories N % N % N % N % N % < % 343 2% 85 1% % 205 1% % 677 4% 115 1% 21 1% 378 2% ,327 9% 1,557 8% % % 1,514 7% ,835 24% 3,685 20% 2,831 34% % 4,526 22% ,593 27% 4,520 25% 2,729 33% % 5,829 28% 65+ 8,757 35% 7,657 42% 1,496 18% % 8,433 40% Unknown % % % % % TOTAL 24,716 18,440 8,240 1,964 20,885 15

16 PaPent CharacterisPcs: LocaPon Substance Use Disorder Diagnosis Behavioral Health Diagnosis Mental Health Diagnosis Co-Occurring Disorders Comorbid Condi%ons Loca%on N % N % N % N % N % Montgomery County 10,851 44% 8,710 47% 2,777 34% % 8,678 42% Philadelphia County 7,249 29% 4,937 27% 3,132 38% % 6,649 32% Other/Unknown 6,616 27% 4,793 26% 2,331 29% % 5,558 27% TOTAL 24,716 18,440 8,240 1,964 20,885 16

17 PaPent CharacterisPcs: Race & Ethnicity Race: Pa6ents iden6fying as White (83%) or Black (9%) represent over 90% of pa6ents with BH diagnosis Ethnicity: Over 90% of pa6ents with BH diagnosis are non- Hispanic; important to note 10% of pa6ents ethnicity is listed as Unknown 17

18 Most Prevalent Mental Health Diagnoses Top Mental Health Diagnoses N % of MH diagnoses (N = 32,603) Anxiety disorder, unspecified 6,735 21% Major depressive disorder 6,005 18% Unspecified demen6a without behavioral disturbance 3,673 11% 18

19 Most Prevalent Substance Use Disorder Diagnoses/CondiPons Top Substance Use Disorder (SUD) Diagnoses/Condi%ons N % of SUD diagnoses (N = 11,769) Nico6ne dependence, cigareses 4,173 35% Nico6ne dependence, unspecified 1,322 11% Overdose* 660 6% *Not diagnos6c code but free-text 19

20 Comorbid CondiPons BH Diagnoses & Comorbid Physical Diagnoses BH Diagnosis 1 st Most Prevalent Physical Diagnosis 2 nd Most Prevalent Physical Diagnosis F anxiety disorder I10 - hypertension E hyperlipidemia F major depressive disorder F nio%ne dependence I10 - hypertension I10 - hypertension E type 2 diabetes E hyperlipidemia 3 rd Most Prevalent Physical Diagnosis K gastro-esophageal reflux disease J chronic obstruc6ve pulmonary disease R unspecified abdominal pain 20

21 PotenPal Target PrioriPes Diagnosis/Condi%on: Anxiety disorder, major depressive disorder, overdose, crisis Loca%on: Philadelphia residents, par6cularly those with substance use diagnosis, co-occurring disorder Pa6ents with comorbid condi%ons Pa6ents with visits to mul%ple facili%es 21

22 A Few Concluding Thoughts Working with HSX not only enabled access to data in a secure and 6mely fashion, but also made possible analyses of encounters across mul6ple facili6es Challenge of underes6ma6ng BH diagnoses in the ED Repurposing data obtained through one use case (in this case, encounter no6fica6on service) has limita6ons for other uses Importance of consistency and improved data quality of ADT feeds if data is to be used for popula6on health and other purposes 22

23 Thank You! If you have ques%ons, please contact: Susan Choi, PhD, CPHQ Senior Director, PopulaWon Health Health Care Improvement Founda6on