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A Mixed Methods Study of Information Availability on Pregnancy Outcomes

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

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A Mixed Methods Study of Information Availability on Pregnancy Outcomes

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  1. 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

  2. Acknowledgements • This research received financial support from Agency for Healthcare Research and Quality (AHRQ) Grant PARA-08-270 and from a Lehigh University Faculty Innovation Grant

  3. Objective • 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 • 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 • Qualitative methods to measure barriers to data access and perceptions

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

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

  6. Empirical models Model 1: Linear probability model (LPM) & regression • 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) • 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

  7. WAOS > 0 Notes: Percentage pt. and percentage effects with clustered standard errors in brackets

  8. Notes: Percentage pt. and percentage effects with clustered standard errors in brackets

  9. Office models - WAOI Notes: Percentage pt. and percentage effects with clustered standard errors in brackets

  10. Office models – Obst. trauma & payments Notes: Percentage pt. and percentage effects with clustered standard errors in brackets

  11. 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

  12. 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) Physician vs. staff perceptions 2010 Office Data: N=89 (74 staff; 15 physicians))

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