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Logo of jamiaJAMIA - The Journal of the American Medical Informatics AssociationInstructions for authorsCurrent TOC
J Am Med Inform Assoc. 2007 Jul-Aug; 14(4): 432–439.
PMCID: PMC2244900

Adoption of Order Entry with Decision Support for Chronic Care by Physician Organizations

Abstract

Objective

This study sought to explore physician organizations’ adoption of chronic care guidelines in order entry systems and to investigate the organizational and market-related factors associated with this adoption.

Design

A quantitative nationwide survey of all primary care medical groups in the United States with 20 or more physicians; data were collected on 1,104 physician organizations, representing a 70% response rate.

Measurements

Measurements were the presence of an asthma, diabetes, or congestive heart failure guideline in a physician organization’s order entry system; size; age of the organization; number of clinic locations; type of ownership; health maintenance organization market penetration; urban/rural location; and presence of external incentives to improve quality of care.

Results

Only 27% of organizations reported access to order entry with decision support for chronic disease care. External incentives for quality is the only factor significantly associated with adoption of these tools. Organizations experiencing greater external incentives for quality are more likely to adopt order entry with decision support.

Conclusion

Because external incentives are strong drivers of adoption, policies requiring reporting of chronic care measurements and rewarding improvement as well as financial incentives for use of specific information technology tools are likely to accelerate adoption of order entry with decision support.

Introduction

Health care organizations are increasingly turning to clinical information technology (IT) to improve the quality and efficiency of care they provide. But, little is known about which IT components are commonly used or how they are incorporated into practice. For example, recent studies have estimated that 17% to 25% of physician practices use electronic medical records (EMRs), 1–3 but these studies do not indicate which EMR components are being used within the organization, nor is there an indication of how these tools are being used to improve patient care. As defined by the Institute of Medicine, an EMR consists of “electronically stored information about an individual’s lifetime health status and health care. It replaces the paper medical record as the primary record of care, meeting all clinical, legal, and administrative requirements. An [EMR] system provides reminders and alerts, linkages with knowledge sources for decision support, and data for outcomes research and improved management of health care delivery”. 4 The Institute of Medicine definition describes some of the wide array of functions which may be included in an EMR, but the capabilities of EMRs that are actually implemented in physician practices vary a great deal, and it is unclear whether EMRs in physician practices are primarily configured for the basic purposes of recording and accessing information or whether more complex components are typically incorporated in the EMR’s capabilities. This article presents data on the extent to which physician organizations have adopted several components designed to improve chronic disease care: order entry with embedded decision support for asthma, diabetes, and congestive heart failure. We also assess the organizational and market-related factors associated with adoption of these tools.

Background

Much of the previous research on EMRs has focused on two components, computerized physician order entry (CPOE), the ability for providers to write orders such as prescriptions and laboratory tests electronically, and clinical decision support systems (CDSSs). A CDSS synthesizes, integrates, and evaluates patient-specific information and provides the results to clinicians in a timely fashion, often in the form of alerts and reminders. 5 Adoption of both tools has been advocated to improve quality of care and patient safety. 6 Although there are good reasons to believe that CPOE and CDSSs can improve quality and safety, existing research studies present a somewhat complicated picture.

Numerous studies have demonstrated the ability of CPOE or CDSSs to reduce error rates and improve care processes in hospitals. 7–10 However, controversy has recently emerged surrounding the benefits of CPOE in particular. Several studies have reported that CPOE systems facilitate certain types of errors, 11,12 although these studies did not assess whether these negative consequences were outweighed by the benefits created by the system. This controversy has been heightened of late by a study that found an increase in patient mortality subsequent to the implementation of a CPOE system in a tertiary care children’s hospital. 13 Critics of the study point to methodological problems such as an inability to control for other changes within the hospital and inadequate time to resolve implementation problems before gathering post data. 14 In contrast, a more recent study of the same CPOE system in a similar setting found a nonsignificant reduction in risk of patient mortality in the 13 months after implementation. 15

The literature on the impact of these systems on compliance with chronic disease guidelines in an ambulatory setting also shows mixed results. For example, a randomized controlled trial of the effects of a CDSS found a 2-fold improvement in compliance with a diabetes care guideline. 16 This study was conducted at a primary care clinic. There were several limitations, however, including a small sample size of only 30 primary care clinicians. In addition, the guidelines were computer generated but delivered as a paper-based encounter form attached to the patient chart. There may be differences in how physicians respond to decision support transmitted via computer as opposed to decision support generated by a computer but delivered on paper.

In contrast, another study of CDSSs and diabetes guideline compliance found no impact of CDSSs. 17 This randomized controlled trial conducted in primary care clinics in Norway assessed the impact of CDSSs on diabetes guideline compliance as well as on patient outcomes. No significant differences between the intervention group and the control group were found in either area. One limitation of this study is the fact that it was conducted in Norway. It is unclear how generalizable the results are to clinics in the United States given differences in the health care systems between the countries and differences in clinic structures.

Similarly, a study of CDSSs for the management of asthma and angina found no impact on guideline compliance. 18 This randomized controlled trial provided automated guidelines at the point of care to physicians treating patients in the intervention group compared with paper guidelines provided to physicians treating patients in the control group. The intervention had no effect on consultation rates, process measures, or patient outcomes.

In summary, the research is inconsistent on how effective clinical IT tools such as CPOE and CDSSs are at improving care for populations with chronic disease. Possibly because of the inconsistent nature of the evidence, there have been few studies specifically describing the extent of adoption of the technologies in physician organizations and how they are using these specific tools when treating patients with chronic disease.

Another gap in the literature is the identification of factors associated with the adoption of these tools by physician organizations. However, there is a body of literature on the factors associated with the adoption of EMRs. This literature is relevant because CPOE and CDSSs are two components commonly found in EMRs. Further, organizations face similar barriers in implementing the CPOE and EMR systems and experience similar types of benefits. Thus the organizational and market-related factors that influence adoption of EMRs are likely to also influence adoption of order entry with decision support.

Organizational size has been consistently identified as a predictor of on EMR adoption. 1,2,19 A systematic review of the literature on the adoption of EMRs by Brailer et al. 2 found that larger organizations were more likely to be adopters in both inpatient and outpatient settings. This review also identified urban versus rural location to be an influential factor, with urban organizations more likely to adopt EMRs. A recent study of physician organizations in particular found size to be an important predictor of adoption, but found that urban versus rural location was not a significant predictor. 1

A qualitative study of barriers to EMR adoption by physician practices found external incentives to be an influential factor. 20 This study interviewed leaders in 30 physician organizations that had implemented an EMR and found that lack of incentives such as financial rewards for quality improvement and public reporting were significant barriers to adoption.

A similar qualitative study of barriers to CPOE adoption in a hospital environment found competitive pressure to be an important motivator of adoption. This study interviewed individuals at 26 hospitals in varying stages of CPOE implementation. A strategy suggested by interviewees to overcome resistance was to use the “threat of market share loss, as motivators in the push to adopt CPOE.” 21

The literature on organizational innovation supports the notion that competitive pressure is a driver of adoption. 22 According to this literature, the need to differentiate one’s firm from competitors is associated with an entrepreneurial spirit and the perceived need for new production processes and products. The literature also suggests that the age of the organization may also be an influential characteristic as younger firms are more likely to have more entrepreneurial spirit.

Hence, both the evidence from studies of EMR adoption as well as the literature on organizational theory point to a number of organizational and environmental characteristics that may be predictors of adoption of order entry with decision support in physician organizations. We discuss a conceptual framework that incorporates these factors.

Conceptual Framework: Organizational and Market-Related Factors Associated with Adoption of CPOE with Integrated CDSSs

Based on existing evidence, we hypothesize that the following organizational and market-related characteristics are associated with adoption of CPOE with integrated CDSSs to support chronic disease care: size, age of the organization, number of clinic locations, type of ownership, extent of managed care penetration in the local market, urban-rural nature of the community, and presence of external incentives for improving quality.

Specifically, we hypothesize that larger organizations are more likely to adopt order entry with decision support. Larger organizations typically have the resources, infrastructure, and staff needed to implement complex technology tools. Based on the literature on organizational innovation, we hypothesize that younger organizations are more likely to use CPOE with integrated CDSSs. In addition to more entrepreneurial spirit, new organizations also are likely to have younger physicians who trained in an era when computers are becoming increasingly common. A greater number of savvy computer users may translate into experimentation with the use of more complex software functions. We expect organizations with fewer clinic locations to be more likely to utilize CPOE with integrated CDSSs. Organizations with providers and resources concentrated in fewer locations face fewer implementation barriers than dispersed organizations. We also hypothesize that organizations owned by health maintenance organizations (HMOs) or hospitals are more likely to use CDSSs and CPOE because they may have greater access to capital, IT staff, and other forms of support. These resources may provide an organization with the support needed to go beyond a basic system implementation and encourage the use of additional functions.

In terms of environmental characteristics, we believe organizations in areas with higher managed care penetration face more competitive pressures. These organizations may view CPOE with CDSSs as means to gain competitive advantage by improving quality and reducing errors.

Because EMR adoption is greater in urban than rural areas, 2 we hypothesize that CPOE with integrated CDSSs is more likely to be implemented in organizations in urban settings. We also believe organizations facing greater incentives to improve quality are more likely to adopt CPOE with integrated CDSSs. External incentives include rewards for scoring well on quality measures or requirements to report quality to outside entities. Access to clinical decision support for orders is an important mechanism for reducing errors and improving adherence to evidence-based processes. If external incentives for improving quality exist, organizations are likely to be highly motivated to adopt these tools.

Methods

The data used in this analysis come from the National Study of Physician Organizations (NSPO), which collected data nationwide on 1,104 physician organizations (738 medical groups and 366 independent practice associations) with 20 or more physicians. 23

Using a pretested and closed-ended set of questions, data were collected on many characteristics of physician organizations, including practice size, ownership, years in existence, type of practice, governance and management, financial management, relationships with health plans, degree of risk assumption, compensation models, external incentives, use of IT, care management processes, and quality improvement approaches. These data were collected by trained interviewers at the National Opinion Research Center at the University of Chicago in 60-minute structured interviews with the chief executive officers, presidents, or medical directors of the physician organizations from September 2000 to September 2001. The 1,104 organizations on which data were collected represent a response rate of 70%. Respondents and nonrespondents did not differ by size or state where they were located. 24,25 Field visits and follow-up telephone interviews conducted in 24 practices permitted validation of the self-report data and further insights into physician organization performance. 26

The outcome variable in the model is a scale measuring the extent of adoption of CPOE with integrated CDSSs for chronic disease care based on responses to the question, “Are guidelines or protocols placed in order entry systems for any of the following chronic diseases: asthma, diabetes, congestive heart failure.” Organizations received 1 point for each chronic disease for which they had a positive response. Only organizations stating that they used guidelines were asked to respond to the question. As a result, only 295 organizations responded to all 3 questions about the presence of CPOE with integrated CDSSs.

To assess the level of external incentives an organization experienced, physician organizations were surveyed on several types of incentives including reporting to outside organizations, bonuses from health plans for performing well on quality measures, public recognition, and better contracts with health plans. An external incentives index with a range of 0 to 7 was created with these data. [triangle] presents the specific questions from the NSPO survey used to operationalize the predictor and dependent variables.

Table 1
Table 1 Survey Questions Used in Predictor and Dependent Variables

Statistical Methods

Because we studied the entire population of physician organizations with 20 or more physicians rather than a randomly selected sample, we did not calculate confidence intervals or statistical tests for our adoption rates. However, statistical analyses were performed to examine the relationships among our predictor variables and adoption of CPOE with CDSSs. Bivariate correlations were examined to identify associations between all variables. We used difference of means tests to assess whether physician organizations with access to CPOE with integrated CDSSs for any condition differed from groups not using these functions on the various organizational and market characteristics specified above. Student’s t-tests were used to compare groups on the continuous variables and χ2 tests to compare groups along categorical variables. We also used an ordinary least-squares regression model to predict the effects of these organizational and market characteristics on the number of chronic care guidelines in order entry systems. We used a logistic regression model to predict the effect of these variables on presence or absence of CPOE with CDSSs for any condition. A p-value of <0.05 was used to determine statistical significance.

Results

Of the 295 organizations responding to the questions, 215 organizations (73%) did not have guidelines in order entry systems for any of the 3 conditions. Sixteen percent or 42 of the responding organizations had CPOE with integrated CDSSs for all 3 conditions. [triangle] shows a frequency table of the number of conditions for which organizations had embedded guidelines in its order entry system. Ninety-nine organizations, or 18% of those respondents, reported having guidelines in order entry systems for diabetes ([triangle]). Similarly, 96 organizations (18%) reported decision support in order entry for asthma. Eighty-seven organizations (20% of respondents) reported availability of CPOE with CDSSs for congestive heart failure.

Table 2
Table 2 Frequency of the Number of Chronic Care Guidelines Embedded in CPOE System
Table 3
Table 3 Number of Organizations Adopting Chronic Care Guidelines in Order Entry Systems

The bivariate correlations between each of the measures are presented in [triangle]. Adoption of CPOE with integrated CDSSs for the 3 chronic conditions is significantly correlated with organization age (r = 0.13, p < 0.05), number of clinic locations (r = 0.13, p < 0.05), and the presence of external incentives for quality (r = 0.22, p < 0.001). External incentives was significantly correlated with a number of other characteristics including organization size (r = 0.08, p < 0.01), hospital or HMO ownership (r = 0.11, p < 0.01), percent of HMO penetration in the county (r = 0.20, p < 0.001), and urban versus rural location (r = 0.10, p < 0.001).

Table 4
Table 4 Pearson Correlations

Differences between Groups Adopting and Not Adopting CPOE with CDSSs

Difference of means tests between physician organizations with guidelines in order entry systems for any of the 3 conditions and those that do not have it for any condition are presented in [triangle]. This table provides the means for all independent variables for organizations with guidelines in order entry systems as well as the means for organizations without access to these tools. The organizations that have CPOE with integrated CDSSs differed from those that do not on the external incentives index (p < 0.05). Groups with the tools scored a mean of 2.6 (SD 2.4) on the index, whereas those without the tools had a mean of 2.0 (SD 1.8). Of the components in the index, those relating to reporting to outside entities were the significant factors. Groups that have incorporated guidelines into CPOE systems were required to report patient satisfaction results, for a mean of 28% of patients (SD 42), whereas those that have not incorporated guidelines were required to report for a mean of 16% of patients (SD 33). Groups with the tools were required to report the results of quality improvement projects, for a mean of 29% (SD 42) of patients, whereas those without them were required to report for a mean of 16% of patients (SD 32). There was also a significant difference in mean percentage of patients for which the organization is required to report outcomes data (p < 0.01); the mean for groups adopting CPOE with CDSSs was 23% (SD 38), versus 12% (SD 29) for groups not adopting the technology.

Table 5
Table 5 Difference in Means Tests Comparing Organizations with Guidelines in Order Entry Systems to Those Without

No significant differences were found for any of the other characteristics, although size of the organization reached borderline significance (p < 0.10). Organizations adopting CPOE with integrated CDSSs had a mean of 320 physicians (SD 569), and those not adopting the technology had a mean of 222 physicians (SD 410).

Multivariate Results

[triangle] presents the results of the multivariate linear regression model predicting CPOE with CDSSs adoption by physician organizations (F = 3.07, p < 0.004, R2 = 0.07). External incentives is the only factor significantly associated with the adoption of guidelines for order entry. Each additional point on the external incentives index was associated with the presence of guidelines in order entry for 0.12 additional chronic diseases. The strong association between external incentives and CPOE with integrated CDSSs was found in every model tested. A logistic regression model using the presence or absence of CPOE with integrated CDSSs as the dependent variable had similar results ([triangle]).

Table 6
Table 6 OLS Regression for Organizational and Market-Related Factors Affecting Physician Organization Adoption of CPOE with Integrated CDSS (F = 3.07, p < 0.004, R2 = 0.07)
Table 7
Table 7 Logistic Regression for Organizational and Market-Related Factors Affecting the Adoption of CPOE with Integrated CDSSs

Discussion

The strong association between external incentives and order entry with decision support is consistent with other research on clinical IT in physician organizations. 20 Although previous research has focused on clinical IT in general or on EMRs, this research adds to the evidence by examining factors associated with more specific functions of the technology, decision support for order entry. It further examines the adoption of these tools to support chronic disease care. Unfortunately, these tools are not available to the vast majority of physician organizations, as evidenced by the fact that 73% of organizations with guidelines have not incorporated them into order entry systems. Guidelines for chronic conditions are becoming more prevalent in physician organizations; 78% of the organizations in the NSPO study reported using guidelines for at least 1 of the 3 chronic conditions. This study illustrates that despite the existence of these guidelines in the strong majority of organizations, they are not typically being implemented in order entry systems.

The organizations that are leveraging these guidelines via clinical IT are more likely to be those operating in environments with stronger external incentives to improve quality. External incentives such as health plan incentive payments for improvement in chronic care measures could be expanded to include specific clinical IT measures. For example, the California Pay-for-Performance Program includes an IT domain in its measurement set. Preliminary analysis of the first 2 years of the program found a significant increase in the number of groups qualifying for rewards based on the IT measures. This finding and interviews with participating physician group leadership appear to indicate that the incentive is motivating increased investment in technology. 27

Interestingly, our findings indicate that an external reporting requirement is a particularly powerful external incentive, more so than additional income for scoring well on quality or better contacts with health plans. Thus the California Pay-for-Performance program, which combines clinical reporting requirements with specific rewards for use of IT, appears to be an excellent model.

Rewarding organizations for adopting technology tools will enable organizations to show improvement in a shorter time frame than with typical chronic disease quality indicators, and thus may be an even more influential motivator. Increased utilization of tools such as order entry with decision support would lead to improvements in guideline adherence, and in the longer term to improvements in measures of health status.

Limitations

This study has several limitations. Because the data used in the study were collected in 2000–2001, there is a risk that our findings may not reflect current levels of technology adoption. We are aware that some modest increase in the implementation of CPOE and CDSSs in physician practices may have occurred since 2001, but there is no evidence that there has been a substantial change in the uptake of these tools in physician practices. A recent systematic review of EMR surveys from 1995–2005 found fairly consistent results across this time period. Most estimated the level of adoption by physicians to be in the 17% to 25% range. 3 We also believe that the factors we have identified to be associated with the variation in adoption have changed very little over the past 6 years. Data from this period also are valuable due to recent changes in federal regulations that allow hospitals to provide some financial assistance to physicians to help them acquire and implement clinical IT tools. The findings from this baseline study will provide an important benchmark against which future adoption and implementation could be compared.

The generalizability of the study is limited by the fact that only physician organizations with at least 20 physicians were surveyed. It is likely that very small organizations are influenced by additional or different factors in deciding to adopt and use clinical IT tools. It is possible that lack of infrastructure and resources may be these organizations’ only significant barrier. 20 Further, the available data did not provide insight into the level of clinical IT use within the organization. The survey did not query the extent of utilization by clinicians or the percentage of clinicians within the organization adopting the tools. Thus it is possible that organizations reporting to have guidelines in order entry systems may not have widely implemented the tool throughout the organization. In addition, the primary respondents to the NSPO survey were physician organization chief executive officers, presidents, and medical directors as opposed to individuals responsible for day-to-day oversight of clinical IT tools. It is possible that different information would have been reported if, for example, a chief information officer had been interviewed. We were unable to include financial variables in this analysis. Although the NSPO survey did attempt to gather limited financial information from organizations, many were unable or unwilling to respond to these questions. The number of missing observations for these variables made it impossible to include this information in the model.

In conclusion, order entry with decision support offers physician organizations a valuable strategy for improving adherence with evidence-based practices and ultimately improving quality of care. The vast majority of organizations have not adopted these tools to help manage chronic disease populations. The strong relationship between external incentives for quality and the availability of order entry with decision support suggests that environmental factors play an important role in physician organizations’ decisions to adopt this type of technology. Policies requiring reporting of chronic care measurements and rewarding improvement as well as financial incentives for the use of specific clinical IT tools are likely to accelerate adoption in physician organizations.

Footnotes

Grant support was provided to the University of California, Berkeley, by the Robert Wood Johnson Foundation, grant No. 018690.

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