A Mixed Methods Study of Information Availability on Pregnancy Outcomes Chad Meyerhoefer, Susan Sherer, Mary Deily, Shin-Yi Chou Lehigh University Donald Levick, Michael Sheinberg Lehigh Valley Health Network

Acknowledgements This research received financial support from Agency for Healthcare Research and Quality (AHRQ) Grant PARA and from a Lehigh University Faculty Innovation Grant This research received financial support from Agency for Healthcare Research and Quality (AHRQ) Grant PARA and from a Lehigh University Faculty Innovation Grant

Objective Determine the value of timely clinical information at the point of care during pregnancy Determine the value of timely clinical information at the point of care during pregnancy –Inpatient labor and delivery (L&D) unit –Outpatient OB/GYN offices –Change in data availability due to EHR implementation Quantitative methods to measure impact of data availability on pregnancy outcomes & payments Quantitative methods to measure impact of data availability on pregnancy outcomes & payments –3 rounds of data collection on the L&D and at OB/GYN offices –Adverse outcomes data collected through chart review Other measures extracted from billing data Other measures extracted from billing data Qualitative methods to measure barriers to data access and perceptions Qualitative methods to measure barriers to data access and perceptions

Data & patient flow during pregnancy Outpatient practice CPO Triage Unit CPN Labor & Delivery CPN Mother - Baby Unit Lastword / CPN Discrete Summary Patient flow Data flow HOSPITAL DOCTOR’S OFFICE

Increase in data availability in Triage Average monthly N = 119, Max = 193, Min = 16

Empirical models Model 1: Linear probability model (LPM) & regression Outcomes (Triage: N=1,324; Offices: N=1,809) Outcomes (Triage: N=1,324; Offices: N=1,809) – Obstetric trauma (0/1), mean = 0.06 – Log(payments), mean = 8.8 ($6,336) Model 2: Two-part model (LPM & Log OLS) Outcomes (Triage: N=1,324 / 99; Offices: N=1,809 / 119) Outcomes (Triage: N=1,324 / 99; Offices: N=1,809 / 119) –Weighted adverse outcome score (WAOS) (0/1) > 0, mean = 0.08 – Log(WAOS), mean = 3.1 Control variables –DCG/HCC risk score quartile, age, race/ethnicity, insurance type, admission type, multiple birth, pre-existing condition, non- preventable complication, c-section, instrument assisted delivery, indicators for data elements in system (Triage), physician fixed effects

WAOS > 0 Clinical data elementsWAOS > 0Log(WAOS) (=1 if available for review in L&D Triage) Parsimoniou s model Saturated model Parsimoniou s model Saturated model Cervical exam [0.02] [0.31][0.42] Blood pressure **-0.54 [0.02][0.03][0.31][0.65] Antenatal prob. list ***-0.76** [0.02][0.03][0.26][0.33] Nonstress test [0.03] [0.39][0.52] Prior uterine incision type [0.03][0.04][0.61][0.64] Group B strep status [0.03] [0.49][0.41] Tubal sterilization -0.06**-0.05** request (Medicaid) [0.03] [1.54][1.35] Notes: Percentage pt. and percentage effects with clustered standard errors in brackets

Clinical data elementsObstetric traumaLog(Payments) (=1 if available for review in L&D Triage) Parsimoniou s model Saturated model Parsimoniou s model Saturated model Cervical exam -0.03* [0.02] [0.06] Blood pressure -0.04* * [0.02][0.03][0.06][0.05] Antenatal prob. list **-0.16** [0.02][0.03][0.07] Nonstress test -0.06** [0.03] [0.10][0.12] Prior uterine incision type [0.03][0.04][0.16][0.18] Group B strep status [0.02][0.03][0.09][0.11] Tubal sterilization -0.05*-0.06*0.00 request (Medicaid) [0.03] [0.21] Notes: Percentage pt. and percentage effects with clustered standard errors in brackets

Office models - WAOI Clinical data elementsWAOS > 0Log(WAOS) (=1 if available for review in the OB/GYN Office) Parsimoniou s model Saturated model Parsimoniou s model Saturated model New diagnoses *** [0.03] [0.74][0.53] Cervical exam [0.02][0.03][0.48][1.40] Nonstress test [0.02][0.04][0.54][0.89] Lab work *0.85 [0.02] [0.49][1.53] Notes: Percentage pt. and percentage effects with clustered standard errors in brackets

Office models – Obst. trauma & payments Clinical data elementsObstetric traumaLog(Payments) (=1 if available for review in the OB/GYN Office) Parsimoniou s model Saturated model Parsimoniou s model Saturated model New diagnoses *0.19* [0.03][0.04][0.08][0.09] Cervical exam *-0.29* [0.02] [0.15][0.16] Nonstress test * [0.02] [0.14] Lab work [0.02] [0.12][0.14] Notes: Percentage pt. and percentage effects with clustered standard errors in brackets

Provider interviews Barrier to data access: Trust I don’t trust anything or anyone or anything automatically flowing - Physician Greater data availability through EHR Many times a patient would be seen in Triage in the interval between their visits, and you wouldn’t even know it. So at least seeing that document triggers you to say, “oh, well she was in … triage. Why was she there?” - Physician

Physician vs. staff perceptions 2010 Office Data: N=89 (74 staff; 15 physicians) ) Physicians perceive limited availability of information from Triage at offices & find it more difficult to use EHR (1 = Agree strongly that EMR improves [ ]; 5 = Disagree strongly)