The Impact of Health Information Technology on Demand for Inpatient Services (Minnesota)

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Summary:

The influence of health information technology (IT) on where consumers decide to receive hospital inpatient services is largely unknown. The decisions consumers make can affect the cost and quality of those services as well as the market power of the hospitals. The demand analysis completed in this project complements existing supply-side analyses to provide more complete and dynamic estimates of the impact that health IT has on health care markets. The analysis required information about hospitals’ characteristics and information technology as well as patients’ characteristics and hospital choices from 1999 through 2006. Data needed to perform this analysis came from several sources. Hospital characteristics data were obtained from the American Hospital Association (AHA) annual survey. This database contains information on hospitals’ physical and organizational characteristics such as location, number of full-time physicians, and number of beds. The AHA database was linked with the Health Information and Management Systems Society Analytics Database. This dataset contains detailed historical information on the health IT software, hardware, and infrastructure installed in the surveyed hospitals. Three types of health IT were evaluated: electronic medical records, computerized physician order entry, and picture archiving systems. Inpatient Medicare claims data are the source of patient-level characteristics and hospital choices. 

The project aims included:

  • Measure the effect of hospital adoption of health IT on the demand for inpatient care.
  • Estimate the impact of health IT by type of inpatient service.
  • Evaluate the effect of changes in patient hospital choices using consumer surplus as a welfare measure.

The demand for hospital inpatient services was estimated using standard econometric choice models that included patient characteristics, hospital characteristics, and observed patient choices. A hospital’s decision to implement health IT was considered a treatment or policy intervention, and the change in the total number of patients using the hospital was the outcome of interest. A discrete choice model was estimated using patient-level data to predict the probabilities of patients choosing each hospital in their choice set. The parameter estimates from these models show how health IT affects a patient’s likely hospital choice. An aggregate level model was employed in situations where the data set was too large to estimate at an individual patient level. 

The impact of health IT was small, if it existed, in that it was not found to affect large numbers of patients’ choices or have a large impact on overall hospital demand. The picture archiving systems variables and interaction terms in both the market level and individual choice models are jointly significant and expected consumer surplus is positive. Effects of the other technologies on demand were not significant. Although the value of health IT is positive, for some types of health IT, the effect on market share may not be enough to justify the financial investments.

The Impact of Health Information Technology on Demand for Inpatient Services - 2011

Summary Highlights

  • Principal Investigator: 
  • Funding Mechanism: 
    PAR: HS06-118: AHRQ Grants for Health Services Research Dissertation (R36)
  • Grant Number: 
    R36 HS 018272
  • Project Period: 
    September 2009 - April 2011
  • AHRQ Funding Amount: 
    $24,642
  • PDF Version: 
    (PDF, 189.6 KB)

Summary: The influence of health information technology (IT) on where consumers decide to receive hospital inpatient services is largely unknown. These decisions can affect the cost and quality of those services as well as the market power of hospitals. This project examined the role of health IT on inpatient health care service demand. This demand analysis complements existing supply-side analyses to provide more complete and dynamic estimates of the impact that health IT has on health care markets. It allows policymakers and the health care industry to make better decisions regarding optimal health IT adoption and implementation strategies.

The analysis required information about hospitals' characteristics and information technology as well as patients' characteristics and hospital choices from 1999 through 2006. Data needed to perform this analysis came from several sources. Hospital characteristics data were obtained from the American Hospital Association (AHA) annual survey. This database contains information on hospitals' physical and organizational characteristics such as location, number of full-time physicians, and number of beds. The AHA database was linked with the Health Information and Management Systems Society Analytics Database. This dataset contains detailed historical information on the health IT software, hardware, and infrastructure installed in the surveyed hospitals. Three types of health IT were evaluated: electronic medical records, computerized physician order entry, and picture archiving systems. Inpatient Medicare claims data were the source of patient-level characteristics and hospital choices.

The demand for hospital inpatient services was estimated using standard econometric choice models that included patient characteristics, hospital characteristics, and observed patient choices. A hospital's decision to implement health IT was considered a treatment or policy intervention, and the change in the total number of patients using the hospital was the outcome of interest. A discrete choice model was estimated using patient-level data to predict the probabilities of patients choosing each hospital in their choice set. The parameter estimates from these models show how health IT affects a patient's likely hospital choice. An aggregate level model was employed in situations where the data set was too large to estimate at an individual patient level.

Specific Aims:

  • Measure the effect of hospital adoption of health IT on the demand for inpatient care. (Achieved)
  • Estimate the impact of health IT by type of inpatient service. (Achieved)
  • Evaluate the effect of changes in patient hospital choices using consumer surplus as a welfare measure. (Achieved)

2011 Activities: Model specifications were tested during this period to estimate the magnitude of the variables in the model. The impact of each of the variables was determined by the size and significance of the model parameter estimates. The project evaluated changes in patient hospital choices using consumer surplus as a welfare measure, and measured the effect of hospital adoption of health IT on the demand for inpatient care. A 6-month no-cost extension period was used to complete the data analysis. The project was completed by the end of 2011.

Preliminary Impact and Findings: The impact of health IT was small, if any, because it was not found to affect large numbers of patients' choices or have a large impact on overall hospital demand. The picture archiving systems variables and interaction terms in both the market level and individual choice models are jointly significant and expected consumer surplus is positive. Effects of the other technologies on demand were not significant. Although the value of health IT is positive, health IT's effect on market share may not be enough to justify the financial investments.

Target Population: Elderly*, Medicare

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decision making through the use of integrated data and knowledge management.

Business Goal: Knowledge Creation

* This target population is one of AHRQ's priority populations.

The Impact of Health Information Technology on Demand for Inpatient Services - 2010

Summary Highlights

  • Principal Investigator: 
  • Funding Mechanism: 
    PAR: HS06-118: AHRQ Grants for Health Services Research Dissertation (R36)
  • Grant Number: 
    R36 HS 018272
  • Project Period: 
    September 2009 – April 2011, Including No-Cost Extension
  • AHRQ Funding Amount: 
    $24,642
  • PDF Version: 
    (PDF, 357.36 KB)


Target Population: Elderly*, Medicare

Summary: The influence of the adoption of health information technology (IT) on where consumers decide to receive hospital inpatient services is largely unknown. These decisions affect the costs and quality of those services and the market power of the hospitals. This project is examining the role of health IT in meeting inpatient health care service demands. This demand analysis complements existing supply-side analyses to provide more complete and dynamic estimates of the impact that health IT has on health care markets. It allows policymakers and the health care industry to make better decisions on optimal health IT adoption and implementation strategies.

The analysis requires information about hospitals’ characteristics and information technology as well as patients’ characteristics and hospital choices. Data needed to perform this analysis comes from several sources. Hospital characteristics data are obtained from the American Hospital Association (AHA) annual survey. This database contains information on hospitals’ physical and organizational characteristics such as location, number of full-time physicians, services provided, and number of beds. The AHA database is linked with the Health Information and Management Systems Society Analytics Database. This dataset contains detailed historical information on the health IT software, hardware, and infrastructure installed in the surveyed hospitals. Inpatient Medicare claims data are the source of patient-level choices and characteristics. Regional demographic data from the 2000 U.S. Census are also included.

The econometric methods for this project estimate the demand for hospital services using patient characteristics, hospital characteristics, and observed patient choices. A hospital’s decision to implement health IT is considered a treatment or policy intervention, and the change in the total number of patients using the hospital is the outcome of interest. A discrete choice model is using patient-level data to estimate the probabilities of patients choosing each hospital in their choice set. The parameter estimates from these models show how health IT affects a patient’s likely hospital choice. Advanced, discrete-choice modeling is applied to deal with biased and inconsistent parameter estimates if they arose.

Specific Aims:
  • Measure the effect of hospital adoption of health IT on the demand for inpatient care. (Ongoing)
  • Estimate the impact of health IT by type of inpatient service. (Ongoing)
  • Evaluate the effect of changes in patient hospital choices using consumer surplus as a welfare measure. (Ongoing)

2010 Activities: All data has been cleaned and merged for the database. Preliminary data analysis using the original model showed that the data sets were too big and were not converging. As a result, additional models and model specifications were tested. The resulting comparable approach used aggregate data to address the issue with data set size. Eight years of data for a subset of three states were ultimately analyzed.

Grantee's Most Recent Self-Reported Quarterly Status (as of December 2010): The project is meeting most of its aims on time. Budget spending is roughly on target. The acquisition of data and model specification took longer than anticipated, resulting in a request for a no-cost extension.

Preliminary Impact and Findings: Preliminary results suggest that health IT does not have a significant effect on patient demand.

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.

Business Goal: Knowledge Creation

*AHRQ Priority Population.

The Impact of Health Information Technology on Demand for Inpatient Services - Final Report

Citation:
Barrette E. The Impact of Health Information Technology on Demand for Inpatient Services - Final Report. (Prepared by the University of Minnesota under Grant No. R36 HS018272). Rockville, MD: Agency for Healthcare Research and Quality, 2011. (PDF, 258.09 KB)

The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.
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