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Impact of Prior Clinical Information in an EHR on Care Outcomes of Emergency Patients

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Theera-Ampornpunt N, Speedie SM, Du J, Park YT, Kijsanayotin B, Connelly DP. Impact of prior clinical information in an EHR on care outcomes of emergency patients. Paper presented at: Biomedical and Health Informatics - From Foundations to Applications to Policy. AMIA 2009 Annual Symposium; 2009 Nov 14-18; San Francisco, CA.

Based on Theera-Ampornpunt N, Speedie SM, Du J, Park YT, Kijsanayotin B, Connelly DP. Impact of prior clinical information in an EHR on care outcomes of emergency patients. AMIA Annu Symp Proc. 2009 Nov:634-8. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815461/

Published in: Health & Medicine
  • Thank you for your interest, Emily. I'm glad that it is helpful. You mentioned modeling of information flow in a clinical setting. We have a poster in last year (2008)'s AMIA that was part of this same project, modeling the information flow in an emergency department. In case it's of your interest, you can check it out at http://www.slideshare.net/nawanan/improving-access-to-clinical-information-in-an-emergency-department-a-qualitative-study-presentation-781814
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Impact of Prior Clinical Information in an EHR on Care Outcomes of Emergency Patients

  1. 1. Impact of Prior Clinical Information in an EHR on C I f ti i Care Outcomes of Emergency Patients AMIA 2009 November 16, 2009 Nawanan Th N Theera‐Ampornpunt, MD, MS A t MD MS Stuart M. Speedie, PhD Jing Du, MPH Young‐Taek Park, MPH Boonchai Kijsanayotin, MD, PhD Donald P. Connelly, MD, PhD Donald P. Connelly, MD, PhD 1
  2. 2. Background • Continuity of care is critical to healthcare y quality & efficiency • Existence of prior history enhances continuity of care, potentially improving quality & efficiency – Preventing redundant tests – Helpful past diagnoses – Allergies & medication lists fewer errors 2
  3. 3. Emergency Departments (ED) • Prone to errors because of – Urgent nature – Limited patient information – Time & resource constraints • Critical transition point from ambulatory to emergency & inpatient settings • Limited/unreliable self reported history self-reported • 32% of ED visits had information gaps which can lead to prolonged ED stay hi h l dt l d t Stiell A et al. CMAJ. 2003;169:1023-8. 3
  4. 4. Mixed Effects of Electronic Records • Information access via HIE leads to more ED visits & hospitalizations among medically indigent adults y g Vest JR. J Med Syst. 2009;33:223-31. • Automated records in inpatient setting p g associated with decreased mortality & costs but no effect on LOS and increased CHF complications Amarasingham R et al. Arch Intern Med. 2009;169:108-14. 4
  5. 5. Study Objective • To evaluate impact of p p prior clinical information readily available in an EHR on q quality & efficiency of care in ED y y • Focus: 3 chronic diseases – Congestive heart failure (CHF) – Diabetes – A th Asthma 5
  6. 6. Methods • Site: 3 large, metro hospital EDs • Time Period: Jun. 2006 - Jun. 2007 • Data Source: Billing & clinical information g systems data • Index Visit: First ED visit in the time period • Internal Patients: Those with at least one substantive encounter in the health system’s EHR prior to the index visit • External Patients: Those without such an encounter prior to the index visit t i t th i d i it 6
  7. 7. Outcome Variables • Duration of ED visit (hours) ( ) • Hospitalization • Hospital length of stay in days (LOS) • Inpatient mortality • Number of lab test orders • Number of diagnostic procedures g (mostly imaging studies) 7
  8. 8. Hypotheses Internal patients (having prior clinical information in an EHR) will exhibit • Shorter ED visit durations • A lower hospitalization rate • Shorter hospital LOS p • A lower inpatient mortality rate • Fewer lab test orders • Fewer procedure orders than external patients p 8
  9. 9. Analysis • Logistic regression for mortality & hospitalization • Generalized linear model for ED & inpatient LOS • Count data models (Poisson, negative binomial, hurdle regression) for counts of lab test orders and procedures • All models adjusted for gender, age, and comorbidities (Charlson index) 9
  10. 10. Descriptive Statistics Characteristic Site 1 Site 2 Site 3 N 1,957 2,050 2,136 Mean age (years) ± SD 57.8 22.6 57 8 ± 22 6 50.9 20.0 50 9 ± 20 0 58.3 22.8 58 3 ± 22 8 % females 59.2% 52.3% 62.2% Mean Charlson index ± SD 1.6 ± 1.3 1.1 ± 0.5 1.3 ± 0.7 % internal patients 70.8% 70 8% 85.0% 85 0% 47.4% 47 4% Hospitalization rate 27.9% 47.2% 61.6% 10
  11. 11. Outcomes: Internal vs External Pts. Outcome Site 1 Site 2 Site 3 Inpatient Mortality Hospitalization C A ED Visit Duration A Inpatient LOS D A Count of C D A Lab Orders Count of D A Procedure Orders A - Asthma C - Congestive Heart Failure D - Diabetes Significant in hypothesized direction Significant opposite hypothesized direction 11
  12. 12. Conclusions • There is some evidence that prior p clinical information in an EHR is associated with improvement in certain p outcomes • But...effects not consistent – Across study sites – Across disease groups • Study shows mixed effects 12
  13. 13. Discussion • Possible underlying organizational y g g characteristics contributing to inconsistent effects – Organizational structures, policies, workflows, and provider practice styles – Differences in how IT is used – Different patient demographics p g p • Variation of effects for different diseases 13
  14. 14. Limitations • Limited availability of potential y p confounders in secondary data • Pattern of information access and use by ED physicians not captured • Heterogeneity among study sites may also contribute to the observed mixed effects 14
  15. 15. Preliminary Results from Second Round of Data (not in p p ) ( paper) • Expanded timeframe, larger sample size • Cl Cleaner data and availability of additional d t d t d il bilit f dditi l data about confounding variables (e.g. race, insurance status, status marital status) • Analysis currently available for 1 study site (Site 3) • Most results are significant in hypothesized directions 15 15
  16. 16. Acknowledgments • Bryan Dowd, Bonnie Westra, Kevin Peterson, and D i l R h f P d Daniel Routhe from University of Minnesota • St ff from 3 participating health systems Staff f ti i ti h lth t • Project’s board members • Thi project was f d d i part under This j t funded in t d grant number UC1 HS16155 from the Agency for Healthcare Research and Quality, Department of Health and Human Services. 16
  17. 17. Outcomes: Internal vs External Pts. Inpatient Inpatient Patient Hospitalization ED LOS Site Mortality y LOS Subgroup S bgro p Odds R ti Odd Ratio Change Ch Odds Ratio Change 1 CHF 0.21 0.82 1.10 0.91 Diabetes 0.63 1.16 1.03 0.70 Asthma N/A 1.42 1.05 1.09 2 CHF 1.56 0.76 1.00 1.09 Diabetes 0.66 0.72 1.10 1.07 Asthma 0.33 0.70 1.08 1.09 3 CHF 0.59 0.35 1.02 1.00 Diabetes 0.57 0 57 1.37 1 37 0.99 0 99 1.17 1 17 Asthma N/A 1.68 1.11 1.21 Cell values represent change in odds or LOS by that factor for internal (vs external) patients Bold significant at p ≤ 0.05 g Significant in hypothesized direction Significant opposite hypothesized direction 17
  18. 18. Outcomes: Internal vs External Pts. Change in Change in Count of Count of Lab Orders Procedure Orders Patient Site Poisson/ Poisson/ Subgroup Logit Logit Negative Negative Part Part Binomial Part Binomial Part 1 CHF 0.70 0.85 0.89 Diabetes 0.71 1.00 1.01 Asthma 0.74 1.06 1.07 2 CHF 0.92 1.21 0.94 Diabetes 1.05 1 05 0.94 0 94 1.31 1 31 0.71 0 71 Asthma 1.09 0.98 1.24 0.47 3 CHF 0.94 0.98 0.46 0.90 Diabetes 0.92 0.97 0.94 0.87 Asthma 0.54 0.99 0.71 1.11 Whether a specific outcome has 1 or 2 parts depends on the best fit count data model Cell values for the logit part represent odds ratio (internal vs external pts.) of having zero count Cell values for the Poisson or negative binomial part represent change in count by that factor for internal patients among patients with positive counts Bold significant at p ≤ 0.05 Hypothesized direction 18 Opposite hypothesized direction

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