Technology Implementation, Experience and Hospital Focus A Longitudinal Analysis of Electronic Medical Records in Acute Care Hospitals

Transcription

1 August 14, 2013 Technology Implementation, Experience and Hospital Focus A Longitudinal Analysis of Electronic Medical Records in Acute Care Hospitals David (Xin) Ding, University of Houston David (Xiaosong) Peng, University of Houston Sarv Deveraj, University of Notre Dame 1

2 Agenda Research Questions Literature Review Research Methods Analysis Results Discussions 2

3 3 Source: Essentials of the U.S. hospital IT Market Applications of the EMRAM by HIMSS Analytics

4 EMRAM Clinical Process /Flow 4

5 Research Questions What are the performance implications of EMR implementation? Does EMR experience lead to additional performance improvements? Does hospital focus affect EMR-performance relationship? 5

6 Literature EMRs and hospital performance Medical errors EMR reduces medical errors by improving the accessibility of progress notes & reducing adverse drug events (Barlow et al. 2004, Sidorov 2006) EMR Increases medical error when not aligned with current existing workflow (Parente et al. 2006, Gonzalez-Heydrich et al. 2000) Hospital efficiency EMR improves efficiency by reducing the retrieval time for patient charts & recordkeeping (Sachs 2003, Furukawa et al. 2010) EMR does not change efficiency (Kazley & Ozcan 2009) Quality EMR improve quality by reducing mortality rate (Thompson et al. 2007) EMR does not improve nurse-sensitive patient outcomes (Furukawa 2011) Financial outcomes EMR leads to better financial outcomes (Hillestad et al. 2005, Walker et al. 2005) EMR leads to greater cost and less desirable financial outcome (Sidorov, 2006) 6

7 Literature (Cont d) RBV Companies achieve competitive advantages through the deployment of valuable, rare, inimitable, and non-substitutable resources and capabilities (Barney, 1991; Peteraf, 1993; Wernerfelt, 1984). Focus Focused factory (Skinner 1974) Hospital clinical focus percentage of patients in a particular hospital in a particular year whose primary diagnosis [ ] falls in the area of (GAO, 2003) focus leads to reduced complexity and increased specialized expertise (Clark and Huckman, 2012) focus results in lower costs (McDermott and Stock, 2011) focus at three levels of the organization is associated with improved outcomes (Kc and Terwiesch, 2011) 7

8 Hypotheses H1: The performance impact of EMRs varies across stages EMRAM (HIMSS, Angst, et al., 2010; Furukawa, et al., 2010a, 2011) Exploitation vs. Exploration (Gary, 2001; Kane and Alavi, 2007) Operational outcomes Performance Clinical & patient outcomes EMR Stages Low High 8

9 Hypotheses (Cont d) H2: EMR experience will have positive impacts of hospital performance contextually embedded nature of technology (Edmondson et al., 2003; Orlikowski, 1993) Operational outcomes Performance Clinical & patient outcomes Experience with EMR Stages High Low 9

10 Hypotheses (Cont d) H3: EMR capabilities will have smaller impacts on performance for hospitals with a higher level of focus. Organizational information process theory (Bozarth and Edwards, 1997; Flynn and Flynn, 2004) Change in Hospital performance Low focus High focus EMR capabilities 10

11 Data Data sources HIMSS Technology application status AHRQ Clinical and patient outcomes CMS Operational characteristics Sample size 12,540 hospital-year observations 1,257 hospitals 2000 to 2009 Level of analysis Yearly data on hospital performance 11

12 Measurements EMR stages* Stage 1 three ancillary departments (i.e., pharmacy, laboratory, and radiology) and a functional Clinical Data Repository (CDR) Stage2 stage 1 plus Nurse Documentation (DOC) and Electronic Medication Administration Records (EMAR) Stage3 stage 2 plus Clinical Decision Support (CDS) and Computerized Physician Order Entry (CPOE). *EMRAM model, Angst, et al., 2010; Furukawa, et al., 2010a, 2010b, 2011 EMR experience cumulative patient discharge after a hospital s complete adoption of EMR applications belonging to each stage Focus HHI ht = Mukherjee et al. (2000) i Bedsize in Department 2 iht Total number of Hospital Beds ht 12

13 Variable Type Variables Description Source Independent EMRSTAGE EMREXP 1 if a hospital has adopted EMR applications for corresponding stage and 0 otherwise Cumulative patient discharges after a complete adoption of EMR applications across stages. HIMSS Angst et al.., (2010) Bower (2005) CMS Operational outcomes OCCUPANCY Patient bed occupancy rate CMS PRODUCTIVITY Ratio between total patient days and FTEs Clinical & Patient outcomes Control variables COST Ratio between total operating expenses and the number of patient discharges, adjusted for Producer Price Index (PPI) MORTALITY The hospital-level mortality rate AHRQ SATISFACTION CASEMIX BED Patient satisfaction, defined as the percentage of high ratings (9-10) among all of the ratings Case mix index, defined as the average diagnosis-related group weight for all of a hospital's patient volume Total number of staffed beds AHRQ, Kutney- Lee et al. (2009) AHA, CMS FTE Full time equivalent employees AGE Hospital age MEDICARE The percentage of patient admissions from Medicare program MEDICAID The percentage of patient admissions from Medicaid program YEAR EXPERIENCE Year dummies The cumulative Service patient Management discharges and from Science the beginning Forum, of the time window (i.e., 2000) to the observed year. 13

14 Variable Summary Statistics Variable Mean S.D COST $20,003 $30, PRODUCTIVITY 11.83% 4.78% *** * p <.05, ** p <.01, *** p < OCCUPANCY 61.01% 18.96% ***.012 *** 4. MORTALITY 1.19% 0.18% *** SATISFACTION 63.37% 8.32%.242 *** *** ** * 6. CASEMIX ***.031 ***.459 *** *** 7. BED (LN) ***.108 ***.572 ***.046 ** *** 8. FTE (LN) *** ***.601 ***.048 **.035 *.705 ***.903 *** 9. AGE (LN) *** ***.112 *** ***.069 ***.119 ***.018 ** 10.MEDICARE 44.47% 15.42% ** *** ***.167 ***.061 *** *** *** *** *** 11.MEDICAID 13.21% 10.01%.018 *.021 *.072 ***.077 *** *** ***.117 ***.119 ***.045 *** *** 12.EMREXP1 (LN) *** ** ***.039 *.029 ** *** *** *** EMREXP3 (LN) ** ***.096 *** * *** 14.EXPERIENCE (LN) *** *** ***.054 **.129 *** ***.198 ***.133 *** ***.047 ***.522 ***.087 *** 14

15 Analysis Method Fixed effect model (FE)* * Hausman test, Wooldridge test, Wald test were conducted to check RE model, autocorrelation, and heteroskedasticity 15

16 Analysis Outputs Cost per Discharge Productivity Occupancy Mortality Satisfaction Intercept 9.72 *** 1.86 *** 1.21 *** 2.81 *** 3.94 *** Control variables CASEMIX.074 * ** BED ***.161 *** *** FTE.028 *** ***.037 *** AGE **.082 **.044 MEDICARE.220 *** *** MEDICAID.126 *.074 ** Year Included Independent variables EMRSTAGE **.067 *** EMRSTAGE ** **.023 *** EMREXP ***.008 **.011 *** EMREXP * **.021 ** EXPERIENCE ** F *** 3.53 ** *** 2.29 ** *** R

17 Analysis Outputs(Cont d) Cost per Discharge Productivity Occupancy Mortality Satisfaction High Low High Low High Low High Low High Low Focus (1b) Focus (1c) Focus (2b) Focus (2c) Focus (3b) Focus (3c) Focus (4b) Focus (4c) Focus (5b) Focus (5c) Intercept 9.46 *** 9.91 *** 2.34 *** 1.44 *** 1.34 *** 1.13 *** 1.98 ** 3.17 ** 4.11 *** 3.82 *** Control variables CASEMIX *8.051 * * BED ***.137 ***.163 *** *** *** FTE.031 *.028 ** *** ***.035 **.037 ** AGE * *.016 *.012 * MEDICARE.245 **.217 ** * * *** *.032 MEDICAID * * * Year Included Independent variables EMRSTAGE * **.035 **.093 *** EMRSTAGE **.011 * **.023 **.021 ** EMREXP ** ***.014 *** ***.011 *** EMREXP * ** ** EXPERIENCE **.001 F *** *** 3.29 ** 2.05 ** *** *** 1.70 * 1.58 ** *** *** R

18 Results Hypotheses Direction 1 The performance impacts of EMRs vary across EMR stages 2 EMR experiences have positive impacts on hospital performance 3 EMR capabilities have a smaller impact on performance for hospitals with a higher level of focus Support? Yes Yes Yes 18

19 Discussion Hospitals persistently develop EMR capabilities can improve performance overtime Expect a long-term payoff instead of short-term returns Both dimensions of EMR capability, namely, EMR stage and EMR experience, help hospitals improve performance Meaningful use of EMR components and integration of EMR applications in routine practice Consider important hospital operational characteristics when evaluating performance impacts of EMRs Focus reflects the degree of operational complexity 19

20 Limitations and Future Directions Sample hospitals count for one third of U.S. hospitals Future studies should expand the selection of hospitals Performance measurements include mortality, cost, productivity, occupancy, and patient satisfaction Future studies should include additional outcome measurements Ten-year operational performance data and three-year clinical outcome data have been used in the model Additional longitudinal datasets can be included to further test the proposed hypotheses 20

21 Q&A 21

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