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Impact of Health IT on Primary Care Workflow and Financial Measures (Text Version)

Slide presentation from the AHRQ 2011 conference.

On September 19, 2011, Neil Fleming made this presentation at the 2011 Annual Conference. Select to access the PowerPoint® presentation (190 KB). Plugin Software Help.

Slide 1

Impact of Health Information Technology (Health IT) on Primary Care Workflow and Financial Measures

Neil S. Fleming, PhD, CQE
Funded by AHRQ (R03HS018220-01)

Track F: Economic Analysis of Health IT
AHRQ 2011 Annual Conference

September 19, 2011

Slide 2


Research Team

  • Edmund R. Becker, PhD.
  • Steven D. Culler, PhD.
  • Dunlei Cheng, PhD.
  • Russell McCorkle, MBA.
  • Briget Da Graca, MS.
  • David J. Ballard; MD, MSPH, PhD.

Slide 3


Study Description

  • Purpose: To estimate the cost and workflow impact of rapid implementation of an electronic health record (EHR) in primary care practices, reducing the uncertainty that health care providers currently face when considering EHR adoption.
  • Setting: 26 HealthTexas (HTPN) primary care practices as part of the Baylor Health Care System, (in North Texas) implementing the electronic health record between January 2004 - December 2009.
  • Methods: We examined pre- and post-implementation billing and administrative data to determine impact on workflow & financial outcomes.
  • Study Design: Quasi-experimental, i.e., natural, retrospective—"...the experimental effect is in a sense twice demonstrated, once against the control and once against the pre-X values in its own series" (Stanley and Campbell, 1966).

Slide 4


Study Data

  • Data related to individual patient visits and revenues were collected from the network billing and collection administrative system.
  • Charges were captured at the procedure code level, and linked to the RVU values, obtained from Ingenix.
  • A single year's RVU scale (2009) was used for all years to make cross-year comparisons on a standardized basis.
  • Specific practice costs and non-physician staffing data, in conjunction with physician staffing data were also captured to compute the measures examined.

Slide 5


Study Design

  • Interrupted time series design with switching replications used here is well-suited to quality improvement research (Cable, 2001).
  • The threat of historical events to internal validity and causal interpretation is reduced compared to single-group pre-post test designs, since the intervention occurs at different times across the full set of included practices; and the design's application to evaluations in "real world settings" that are generalizable to other settings, provides external as well as internal validity. Similarly, the threat to internal and external validity that arises from selection bias is avoided when all practices receive the treatment. The careful application of these time-series methods adheres to proposed guidelines for stronger evidence in the field of quality improvement.

Slide 6


Study Description

  • Workflow:
    • Non-physician Staff per Physician Full-time Equivalent (FTE) (staffing).
    • Work Relative Value Unit (RVU) per Visit (intensity).
    • Work RVU per Physician FTE (productivity).
    • Visits per Physician FTE (volume).
  • Financial:
    • Practice Expense per Work RVU.
    • Practice Expense per Total RVU.
    • Payment Received per Work RVU.
    • Net Income per Work RVU.

Slide 7


Study Description


  • Patient:
    • Age.
    • Sex.
  • Physician:
    • Time at HTPN.
    • Number of physicians.
    • Specialty: Family Practice, Internal Medicine, or Mixed.
    • Year of adoption: 2006/2007 versus 2008.

Slide 8


Study Description

Treatment and Secular Effects:

  • Period of implementation:
    • Prior to implementation.
    • 1-6 months post-implementation.
    • 7-12 months post-implementation.
    • Post 12 months.
  • Secular:
    • Observation period of study: 1-72 months.

Slide 9


Study Description

Statistical Approach:

  • Mixed Linear Model: to analyze 26 practices by month for 72 months from January 2004 through December 2009. Longitudinal data that are correlated within practice, violating independent assumption with simple random sampling:
    • Random Intercept.
    • Random coefficient.

Slide 10


Aims 1 & 2: Study Description

Statistical Model.

We estimated the effects for the following linear model for our work flow and financial measure outcome variables:

  • Yit = β0 + βAEHR*EHR + βT*Tit + βH*H+ εit.
  • where Yit is the work flow or financial measure for practice i (i = 1 to 26 practice); at time t (in months since the beginning of our study in January, 2004).
  • H is a vector of patient and practice level covariates (including the practice characteristics.
  • βT represents the pre-implementation secular trend. Testing H0: βAEHR = 0 for each of the three time periods against the pre-implementation period, we can determine if EHR affects these work flow and financial measures—beyond what we would have observed if the trend had persisted post-implementation.

Slide 11


Means and Standard Errors for the Work Flow and Financial Variables on an Annual Basis

Practice-months (n)1844302312312312309297
 Mean SEMean SEMean SEMean SEMean SEMean SEMean SE
Staff per Physician FTE (n)3.423 (0.025)3.554 (0.067)3.354 (0.060)3.372 (0.057)3.430 (0.063)3.449 (0.059)3.383 (0.057)
Work RVU per visit (RVU)1.052 (0.003)1.051 (0.006)1.064 (0.006)1.065 (0.007)1.045 (0.007)1.035 (0.006)1.050 (0.006)
Visits per Physician FTE (RVU)396.34 (2.425)396.77 (6.226)390.56(5.892)402.51 (5.871)395.69 (6.048)393.22 (5.663)399.42 (5.951)
Work RVU per Physician FTE (RVU)412.29 (2.356)412.42 (5.943)410.77(5.743)423.09 (5.601)408.65 (5.764)403.63 (5.653)415.21 (5.925)
Practice Expense per Work RVU ($)70.35 (0.309)65.61 (0.821)66.83 (0.755)67.79 (0.653)71.35 (0.686)74.94 (0.776)75.71 (0.631)
Practice Expense per Total RVU($)28.52 (0.103)27.61 (0.283)27.82 (0.755)27.73 (0.198)28.65 (0.224)29.79 (0.284)29.54 (0.224)
Payment Received per Work RVU ($)107.44 (0.395)102.44 (0.990)103.69(0.934)103.38 (0.952)110.26 (0.904)109.15 (0.917)111.78 (0.999)

Slide 12


Regression coefficients for change in work flow measures after EHR implementation

 1-6 months7-12 months>12 months
 Regression Coefficient (SE)p-valueRegression Coefficient (SE)p-valueRegression Coefficient (SE)p-value
Staff per Physician FTE0.19 (0.04)<0.0010.10 (0.04)0.0180.12 (0.05)0.007
Work RVU per visit-0.001 (0.006)0.9210.017 (0.01)0.020.003 (0.001)0.683
Visits per Physician FTE-31.99 (4.70)<0.001-29.63 (5.08)<0.001-17.86 (5.36)0.001
Work RVU per Physician FTE-32.84 (4.49)<0.001-22.29 (4.86)<0.001-16.62 (5.15)0.001

Slide 13

Regression coefficients for change in financial measures after EHR implementation

 1-6 months7-12 months>12 months
 Regression Coefficient (SE)p-valueRegression Coefficient (SE)p-valueRegression Coefficient (SE)p-value
Practice Expense per Work RVU3.81 (0.66)<0.0014.05 (0.71)<0.0014.19 (0.75)<0.001
Payment Received per Work RVU-3.03 (0.47)<0.001-3.51 (0.51)<0.001-4.70 (0.54)<0.001
Net Income per Work RVU-3.91 (1.49)0.009-4.25 (1.74)0.146-3.92 (2.05)0.056

Slide 14


Workflow: Productivity (work RVUs per physician FTE) decreased significantly after EHR implementation. Productivity was lowest during the first 6 months following implementation (8% lower), but regained half this ground by 12 months. Volume (visits per physician FTE) followed a similar pattern.

Financial: Practice expense per work RVU showed increases of approximately $4.00 per month over and above the secular trend in each of the 3 periods examined. Based on the monthly mean of 412.3 work RVUs per physician FTE, the increased expense is approximately $1,650 per physician FTE per month. This differential persists for net income per work RVU.

Slide 15


  • Based on our results and other recent reports of financial and productivity effects of EHR implementation in ambulatory care settings, it does not appear that physician practices considering EHR adoption need worry about substantial decreases in productivity or financial performance.
  • While short term decreases are likely (and likely inevitable), we saw substantial recovery in both work flow and financial measures by 12 months post-implementation and other studies report gains in productivity and patient volumes, and decreases in various practice expenses.

Slide 16


  • Financial Alignment is needed between those stakeholders paying for EHRs and those receiving potential benefit:
    • Medicare and Medicaid Meaningful Use Incentive Payments.
    • Private insurer pilots.
  • Some economies of scale can be achieved with larger practices due to fixed and variable nature of some costs.
  • Competitive Forces in the market place will hopefully prevail.
  • Support for and coordination of the medical and technical skills required to ensure successful EHR implementation and physician satisfaction are needed:
    • Regional Extension Centers.
    • Emphasize relationship with software vendor(s).
  • Examine division of labor/work flow within practices to increase productivity and employee satisfaction.
  • Promise of EHR includes clinical decision support & effectiveness research.

Slide 17


Cable G. Enhancing causal interpretations of quality improvement interventions. Qual Health Care Sep 2001;10(3):179-186.

Davidoff F, Batalden P. Toward stronger evidence on quality improvement. Draft publication guidelines: the beginning of a consensus project. Qual Saf Health Care Oct 2005;14(5):319-325.

Mercer SL, DeVinney BJ, Fine LJ, Green LW, Dougherty D. Study designs for effectiveness and translation research: identifying trade-offs. Am J Prev Med Aug 2007;33(2):139-154.

Campbell DT, Stanley JC. Experimental and Quasi-experimental Designs for Research. Chicago: Rand McNally College Publishing; 1966.

Slide 18


Neil S. Fleming, Steven D. Culler, Russell McCorkle, Edmund R. Becker, David J Ballard. The Financial and Nonfinancial Costs of Implementing Electronic Health Records in Primary Care Practices. Health Affairs 30, no. 3 (2011):481-489.

Health Affairs Web presentation and discussion: Innovation in Health Care Delivery, March 8, 2011, in Washington DC.

Page last reviewed October 2014
Internet Citation: Impact of Health IT on Primary Care Workflow and Financial Measures (Text Version). October 2014. Agency for Healthcare Research and Quality, Rockville, MD.


The information on this page is archived and provided for reference purposes only.


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