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Mayo Clinic Proceedings: Innovations, Quality & Outcomes logoLink to Mayo Clinic Proceedings: Innovations, Quality & Outcomes
. 2022 May 31;6(3):269–278. doi: 10.1016/j.mayocpiqo.2022.05.001

Single-Vendor Electronic Health Record Use Is Associated With Greater Opportunities for Organizational and Clinical Care Improvements

Hanadi Y Hamadi a, Shehzad K Niazi b, Mei Zhao a, Aaron Spaulding c,∗
PMCID: PMC9163586  PMID: 35669522

Abstract

Objective

To compare how hospitals that use single-vendor vs best-of-breed electronic health record (EHR) vendors utilize clinical and organizational evaluation capabilities.

Methods

Data from the 2018 (June 1, 2016, to December 31, 2017) American Hospital Association Information Technology Supplement Survey and Medicare Final Rule Standardizing File were used. Multinomial logistic regression analysis of hospitals (n=1902) was conducted to identify hospital characteristics associated with the use of EHRs for (1) clinical care evaluation capabilities and (2) organizational evaluation capabilities.

Results

Single-vendor EHR hospitals were more likely (relative risk ratio, 3.37; 95% confidence interval, 1.97-5.76) to use EHRs for clinical care and organizational evaluation capabilities. Not-for-profit hospitals were more likely to use EHRs for all organizational evaluation capabilities than government nonfederal hospitals. For-profit hospitals were less likely to use EHRs for organizational or clinical evaluation capabilities than government nonfederal hospitals.

Conclusion

Hospitals using the single-vendor EHR system were more likely to engage in clinical care and organizational evaluation than hospitals using best-of-breed EHR systems.

Abbreviations and Acronyms: AHA, American Hospital Association; EHR, Electronic Health Record; HHI, Herfindahl–Hirschman Index; IT, Information Technology; QI, quality improvement; RRR, relative risk ratio; VHA, Veterans Health Affairs

Hospitals and hospital systems increasingly use electronic health record (EHR) systems to support quality improvement (QI) efforts, monitor patient safety, measure organization performance, identify high-risk patients, and improve administrative and clinical processes.1, 2, 3 One common theme across organizational responses is that a greater number of health care delivery systems are addressing resultant or preexisting fragmentation and lack of interoperability in health information technology (IT) through investment in single-vendor as opposed to best-of-breed solutions. A single-vendor solution occurs when the hospital uses the same vendor for all of its EHR needs, whereas a best-of-breed solution is when the hospital integrates the best EHR components from multiple vendors.4, 5, 6 In either case, the solution chosen is focused on how the hospital can best leverage the chosen strategy to reduce costs and improve value-added care.7,8 In many hospitals and health systems, the installation of a new EHR has prompted a shift to a single vendor. For example, over the past decade, health systems have spent considerable financial sums to convert to single-vendor EHR systems, including Mayo Clinic, which reports spending $1.5 billion, and the Kaiser Permanente, spending $4 billion to convert to Epic.9,10 With these vast expenditures, organizations have to believe that there are benefits to pursuing this single-vendor EHR strategy that would otherwise not be achievable or financially viable when considering the best-of-breed solutions.

Although vendors have built a broad range of technologies to facilitate increased value-added care and facilitate patient engagement, wide variation in the adoption of these capabilities by health care organizations exists. This variation has led to questions concerning how hospitals use their EHR when considering best-of-breed vs single-vendor EHRs. Thus, understanding how hospitals are currently using their EHR data is essential both as (1) policy initiatives seek to incentivize hospitals to use their EHR data for performance and population health management and (2) organizations seek to continue to improve both clinical and organizational performance through better leveraging data collected through the EHR. As a result, this study examines how organizations use the EHR capabilities for clinical and organizational evaluation. Specifically, we hypothesize that hospitals with a single-vendor EHR system will be better able to use clinical and organizational evaluation tools.

Methods

Data Source

All hospitals that completed the American Hospital Association (AHA) IT Supplemental from 2018 which includes data from June 1, 2016, to December 31, 2017, were included in the study. The AHA IT survey queries hospitals concerning their EHRs, health information exchange, IT vendors, and how hospitals use data collected through electronic systems.11 Additionally, hospital characteristics were collected from the AHA annual survey, which includes data concerning hospital type, size, and services offered and boasts at least a 75% completion rate.11 Finally, the Medicare Final Rule Standardizing File was used to collect the case mix index and disproportionate share for hospitals within the study.12

Measures: EHR Evaluation Capabilities

Within the AHA IT supplement, hospitals were asked to select all that apply on whether they have used electronic clinical data from the EHR or other electronic system in the hospital to (1) create a dashboard with measures of organizational performance, (2) create a dashboard with measures of unit-level performance, (3) create individual provider performance profiles, (4) create an approach for clinicians to query the data, (5) assess adherence to clinical practice guidelines, (6) identify care gaps for specific patient populations, (7) support a continuous QI process, (8) monitor patient safety (eg, adverse drug events), (9) identify high-risk patients for follow-up care using an algorithm or other tools, or (10) none of the above.

We grouped EHR capabilities into 2 categories; those that focus on organizational evaluation (capability, 1-4) and those that focus on clinical care evaluation (capability, 5-9). We then created 3 ordinal count outcome variables. The first outcome variable was hospitals that used their electronic clinical data from the EHR for organizational evaluation (none = 0 capability, some = 1-3 capabilities, and all = 4 capabilities). The second was hospitals that used their electronic clinical data from the EHR for clinical care evaluation (none = 0 capability, some = 1-4 capabilities, and all = 5 capabilities). Finally, hospitals used their electronic clinical data from the EHR for clinical care and organizational evaluation (none = 0 capability, some = 1-7 capabilities, and all = 9 capabilities).

Measures: Hospital Characteristics

On the basis of the literature, we identified key hospital characteristics that have impacted hospital performance and EHR use. Same or single-vendor EHR environments were identified using the AHA IT survey and operationalized as a binary variable indicating the same EHR vendor was used for inpatient and outpatient care (yes/no). Hospital size is defined as the total number of staffed inpatient beds.13 Hospital ownership is reported as government nonfederal, for-profit, and not-for-profit.14 Teaching hospital status is recorded as teaching vs nonteaching.15 Hospital system membership indicates system members vs a stand-alone facility. The location of hospitals is operationalized by indication of urban (0) vs rural (1) and region (West, Midwest, South, or Northeast). A disproportionate share identifies hospitals serving a large volume of patients who are uninsured or on Medicaid. Similarly, the case mix index is used to control for disease severity at the hospital level. Average Medicare and Medicaid percentages are measured as the proportion of a hospital’s total Medicare or Medicaid visits and total inpatient admissions.16 Finally, market competition for hospitals was measured using the Herfindahl–Hirschman Index (HHI), in which an HHI of 0 indicates a competitive market.17,18

Statistical Analyses

The study population was described by means and counts. Continuous variables were assessed by the Kruskal–Wallis test and categorical variables by the Pearson χ2 test. Multinomial logistic regression analysis assessed the associations between hospital characteristics and EHR capabilities.19 Three separate models were used. Model 1 assessed the use of clinical care evaluation components. Model 2 assessed the use of Organizational Evaluation components, and Model 3 assessed both clinical care and organizational evaluation components. Pairwise deletion was used for missing data, and the data set was reviewed for extreme values that might bias the analysis. STATA 16 was used to run all analyses, and models were estimated through maximum likelihood. Relative risk ratios (RRRs), standard errors, and 95% CIs are reported.

Results

There were a total of 1902 hospitals in our sample, of which 1384 (73%) reported having a single-vendor solution, and 518 (27%) used a best-of-breed solution. Most hospitals have at least 1 EHR capability for either clinical or organizational evaluation (95.74%). Our descriptive analysis (Table 1) reports that 55.89% of hospitals use all EHR clinical care evaluation capabilities, whereas 58.31% use all organizational evaluation capabilities and 46.85% use both clinical and organizational evaluation capabilities.

Table 1.

Description of Electronic Health Records (EHR) Clinical Care, Organizational, or Both Evaluation Capabilities by Categorical Hospital Characteristics, 2018

Clinical care capabilities
 Organizational evaluation capabilities
 All capabilities: clinical and organizational evaluation
None
 1-4
 All
 P value None
 1-3
 All
 P value None
 1-7
 All
 P value
N % N % N % N % N % N % N % N % N %
Categorical, n
 Single-vendor EHR <.001 <.001 <.001
 No 160 31 161 45 197 55 42 8 243 51 233 49 32 6 333 69 153 31
 Yes 248 18 270 24 866 76 59 4 449 34 876 66 49 4 597 45 738 55
 Ownership <.001 <.001
 Government nonfederal 99 35 71 38 114 62 29 10 103 40 152 60 25 9 157 61 102 39
 For-profit 65 23 27 13 186 87 23 8 68 27 187 73 17 6 92 35 169 65
 Not-for-profit 244 18 333 30 763 70 49 4 521 40 770 60 39 3 681 52 620 48
 Member of system <.001 <.001 <.001
 No 172 38 126 46 150 54 55 12 208 53 185 47 47 10 284 71 117 29
 Yes 236 16 305 25 913 75 46 3 484 34 924 66 34 2 646 45 774 55
 Region 0.008 0.489 0.255
 West 67 20 87 33 180 67 19 6 123 39 192 61 16 5 172 54 146 46
 Midwest 97 20 92 24 292 76 19 4 169 37 293 63 16 3 220 47 245 53
 South 189 25 170 30 400 70 43 6 290 40 426 59 34 4 387 53 338 47
 Northeast 55 17 82 30 191 70 20 6 110 36 198 64 15 5 151 48 162 52
 Teaching <.001 <.001 <.001
 No 213 28 192 34 367 66 64 8 304 43 404 57 55 7 407 57 310 43
 Yes 195 17 239 26 696 74 37 3 388 35 705 64 26 2 523 47 581 53
 Rural location <.001 <.001 <.001
 Urban 252 17 315 26 918 74 54 4 509 36 922 64 41 3 680 47 764 53
 Rural 156 37 116 44 145 55 47 11 183 49 187 51 40 10 250 66 127 34
Open in a new tab

Author’s analysis of data (2018) from the American Hospital Association (AHA) Information Technology Supplement Survey, AHA Annual survey, and Medicare Final Rule Standardizing File.

Furthermore, the 891 hospitals that indicated using electronic clinical data from the EHR for clinical and organizational evaluation were largely teaching hospitals, with not-for-profit status, part of a system, and located in urban areas. They also had a noticeably lower average HHI with a mean of 0.37 compared with those hospitals that do not use clinical care evaluation capabilities from their EHR (mean, 0.57), a higher number of staffed beds (mean, 296.65), higher case mix index (mean, 1.68), and the highest disproportionate share at 0.04 (Table 2).

Table 2.

Description of Electronic Health Records (EHRs) Clinical Care, Organizational, or Both Evaluation Capabilities by Continuous Hospital Characteristics, 2018a

Clinical care capabilities
 Organizational evaluation capabilities
 All capabilities: clinical and organizational evaluation
None 1-4 All P value None 1-3 All P value None 1-7 All P value
Continuous, mean (SD)
 Herfindahl–Hirschman index 0.50 (0.42) 0.50 (0.40) 0.37 (0.37) <.001 0.58 (0.41) 0.47 (0.41) 0.38 (0.38) <.001 0.57 (0.42) 0.47 (0.41) 0.37 (0.37) <.001
 Number of staffed beds 177.93 (168.14) 241.61 (237.45) 293.47 (249.58) <.001 124.55 (107.25) 228.35 (201.64) 286.83 (257.3) <.001 107.65 (75.41) 231.88 (218.17) 296.65 (254.18) <.001
 Medicare discharge rate 0.52 (0.15) 0.50 (0.13) 0.51 (0.13) .018 0.51 (0.16) 0.51 (0.14) 0.51 (0.13) .538 0.52 (0.17) 0.51 (0.14) 0.51 (0.13) .471
 Medicaid discharge rate 0.22 (0.15) 0.23 (0.12) 0.21 (0.11) .060 0.22 (0.15) 0.22 (0.13) 0.22 (0.11) .636 0.21 (0.14) 0.22 (0.14) 0.21 (0.11) .150
 Case mix index 1.52 (0.30) 1.60 (0.30) 1.68 (0.29) <.001 1.47 (0.32) 1.59 (0.31) 1.67 (0.29) <.001 1.45 (0.26) 1.59 (0.30) 1.68 (0.29) <.001
Disproportionate share 0.02 (0.02) 0.03 (0.03) 0.04 (0.04) <.001 0.02 (0.02) 0.03 (0.03) 0.04 (0.04) <.001 0.01 (0.01) 0.03 (0.03) 0.04 (0.04) <.001
Open in a new tab

Author’s analysis of data (2018) from the American Hospital Association (AHA) Information Technology Supplement Survey, AHA Annual survey, and Medicare Final Rule Standardizing File.

a

Standard deviation in brackets; significant relationships is at the P<.05 level.

Evaluation Capabilities: Clinical Care

Model 1 (Table 3) identifies which organizations use their EHRs clinical care evaluation capabilities. When comparing which organizations were using “all” vs those who do not use any of their EHR capabilities, we found that hospitals with a single EHR vendor were more likely (ie, had a greater relative risk) to use all EHR capabilities for clinical care evaluation (adjusted RRR, 2.90; 95% confidence interval [CI], 2.19-3.84) compared with hospitals with best-of-breed EHRs. Also, compared with hospitals located in the West region of the United States, hospitals located in the Northeast region were more likely (RRR, 1.78; 95% CI, 1.1-2.89) to have all EHR capabilities for clinical care evaluation. Hospitals with a higher disproportionate share were more likely (RRR, 1.11; 95% CI, 1.01-1.21) to use all EHR capabilities for clinical care evaluation. Hospitals in rural areas had a reduced likelihood (RRR, 0.58; 95% CI, 0.41-0.83) to use all EHR capabilities for clinical care evaluation compared with urban hospitals. Finally, hospitals with higher Medicaid discharge rates were less likely (RRR, 0.25; 95% CI, 0.07-0.98) to use all EHR capabilities for clinical care evaluation compared with those with lower Medicaid discharge rates.

Table 3.

Hospital Characteristics Associated With Electronic Health Record (EHR) Clinical Care or Organizational Evaluation Capabilities, 2018: Multinomial Logistic Regression, N=1902a

Model 1: clinical care capabilities
 Model 2: organizational evaluation capabilities
None vs 1-4
 None vs all
 None vs 1-3
 None vs all
RRR (95% CI) RRR (95% CI) RRR (95% CI) RRR (95% CI)
Single-vendor EHR (referent: no) 1.15 (0.85-1.55) 2.90d (2.19-3.84) 1.39 (0.87-2.22) 2.63d (1.65-4.21)
Ownership (referent: government)
 For-profit 0.40c (0.22-0.72) 1.21 (0.76-1.92) 0.44b (0.21-0.92) 0.55 (0.27-1.13)
 Not-for-profit 1.45 (0.97-2.15) 1.44 (0.99-2.1) 2.36c (1.29-4.32) 1.51 (0.82-2.76)
System member (referent: no) 1.74d (1.26-2.39) 3.24d (2.4-4.37) 2.43d (1.48-3.98) 4.55d (2.78-7.46)
Region (referent: West)
 Midwest 0.71 (0.45-1.14) 1.43 (0.94-2.16) 1.35 (0.65-2.84) 1.88 (0.9-3.91)
 South 0.76 (0.49-1.18) 0.94 (0.64-1.39) 1.34 (0.69-2.58) 1.33 (0.69-2.55)
 Northeast 1.02 (0.61-1.72) 1.78b (1.1-2.89) 0.53 (0.24-1.17) 0.86 (0.39-1.89)
Teaching (referent: no) 0.81 (0.58-1.12) 1.02 (0.77-1.37) 1.09 (0.66-1.8) 1.11 (0.67-1.83)
Rural location (referent: urban) 1.02 (0.69-1.49) 0.58c (0.41-0.83) 0.76 (0.44-1.34) 0.70 (0.4-1.23)
Herfindahl–Hirschman index 0.73 (0.48-1.11) 0.79 (0.54-1.15) 0.92 (0.49-1.74) 0.77 (0.41-1.44)
Staffed beds 1.00 (1.00-1.00) 1.00 (1.00-1.00) 1.01c (1.00-1.01) 1.01c (1.00-1.01)
Medicare discharge rate 0.25b (0.07-0.96) 0.48b (0.14-1.63) 1.65 (0.27-10.21) 2.39 (0.38-14.92)
Medicaid discharge rate 0.47 (0.11-2.02) 0.25 (0.07-0.98) 0.74 (0.09-6.29) 0.94 (0.11-8.07)
Case mix index 0.86 (0.44-1.69) 1.05 (0.59-1.9) 0.66 (0.26-1.69) 0.92 (0.37-2.31)
Disproportionate share 1.14b (1.03-1.26) 1.11b (1.01-1.21) 0.97 (0.77-1.21) 1.05 (0.84-1.3)
Open in a new tab

Author’s analysis of data (2018) from the American Hospital Association (AHA) Information Technology Supplement Survey, AHA Annual survey, and Medicare Final Rule Standardizing File.

a

Relative risk ratio (RRR) exponentiated coefficients; 95% CI in brackets.

b

α<.05.

c

α<.01.

d

α<.001.

When comparing which organizations are using “some” of the EHR capabilities and those who do not use any (none) of the EHR capabilities, we found that compared with government nonfederal hospitals, for-profit hospitals were less likely (RRR, 0.40; 95% CI, 0.22-0.72) to use some EHR capabilities for clinical care evaluation. We also found that hospitals that are part of a system were more likely (RRR, 1.74; 95% CI, 1.26-2.39) to use some EHR capabilities for clinical care evaluation than hospitals that are not part of a system. Hospitals with higher Medicare discharge rates were less likely (RRR, 0.25; 95% CI, 0.07-0.96) to have some EHR capabilities for clinical care evaluation compared with those with lower Medicare discharge rates. Finally, hospitals with a higher disproportionate share were more likely (RRR, 1.14; 95% CI, 1.03-1.26) to use some EHR capabilities for clinical care evaluation.

Evaluation Capabilities: Organizational

Model 2 (Table 3) reports the findings of which organizations that are using their EHR’s organizational evaluation capabilities. When comparing which organizations are using “all” of the EHR capabilities and those that do not use any of the EHR capabilities, we found that hospitals with single-vendor solutions were more likely to use all EHR capabilities for organizational evaluation (RRR, 2.63; 95% CI, 1.65-4.21) compared with hospitals with best-of-breed EHRs.

When comparing which organizations are using “some” of the EHR capabilities and those that do not use any of the EHR capabilities, we found that compared with government nonfederal hospitals, for-profit hospitals were less likely (RRR, 0.44; 95% CI, 0.21-0.92) and not-for-profit were more likely (RRR, 2.36; 95% CI, 1.29-4.32) to use some EHR capabilities for organizational evaluation. We also found that hospitals that are part of a system were more likely (RRR, 2.43; 95% CI, 1.48-3.98) to use some EHR capabilities for clinical care evaluation than hospitals that are not part of a system.

Evaluation Capabilities: Clinical Care and Organizational

Model 3 (Table 4) reports the findings of which organizations are using their EHR’s clinical care and organizational evaluation capabilities. When comparing which organizations used “all” of the EHR capabilities and those that did not use any of the EHR capabilities, we found that hospitals with single-vendor EHRs were more likely to use all the EHR capabilities for clinical care and organizational evaluation (RRR, 3.37; 95% CI, 1.97-5.76) compared with hospitals with best-of-breed EHRs. We also found that compared with hospitals located in the West region of the United States, hospitals located in the Midwest region were more likely (RRR, 2.35; 95% CI, 1.05-5.27) to use all the EHR capabilities for both clinical care and organizational evaluation. Hospitals that are part of a system were more likely (RRR, 6.21; 95% CI, 3.55-10.86) to use all the EHR capabilities for clinical care and organizational evaluation than hospitals that are not part of a system.

Table 4.

Hospital Characteristics Associated With Electronic Health Record (EHR) Clinical Care and Organizational Evaluation Capabilities, 2018: Multinomial Logistic Regression, N=1902a

Model 3: all capabilities
None vs 1-6
 None vs all
RRR (95% CI) RRR (95% CI)
Single-vendor EHR (referent: no) 1.30 (0.78-2.17) 3.37d (1.97-5.76)
Ownership (referent: government)
 For-profit 0.46 (0.21-1.03) 0.82 (0.36-1.86)
 Not-for-profit 2.02b (1.06-3.84) 1.69 (0.86-3.32)
System member (referent: no) 2.73d (1.6-4.67) 6.21d (3.55-10.86)
Region (referent: West)
 Midwest 1.41 (0.64-3.1) 2.35b (1.05-5.27)
 South 1.43 (0.71-2.89) 1.53 (0.74-3.15)
 Northeast 0.60 (0.26-1.43) 1.17 (0.48-2.84)
Teaching (referent: no) 1.16 (0.67-2.03) 1.25 (0.7-2.2)
Rural location (referent: urban) 0.83 (0.45-1.51) 0.62 (0.33-1.16)
Herfindahl–Hirschman index 0.99 (0.5-1.95) 0.89 (0.44-1.79)
Staffed beds 1.01c (1.00-1.01) 1.01c (1.00-1.01)
Medicare discharge rate 1.32 (0.19-9.05) 1.74 (0.23-12.94)
Medicaid discharge rate 1.20 (0.12-12.4) 0.57 (0.05-6.51)
Case mix index 0.72 (0.27-1.93) 0.90 (0.33-2.45)
Disproportionate share 1.03 (0.78-1.36) 1.09 (0.82-1.44)
Open in a new tab

Author’s analysis of data (2018) from the American Hospital Association (AHA) Information Technology Supplement Survey, AHA Annual survey, and Medicare Final Rule Standardizing File.

a

Relative risk ratio (RRR) exponentiated coefficients; 95% CI in brackets.

b

α<.05.

c

α<.01.

d

α<.001.

When comparing which organizations were using “some” of their EHR capabilities and those that do not use any of the EHR capabilities, we found that compared with government nonfederal hospitals, not-for-profit hospitals were more likely (RRR, 2.02; 95% CI, 1.06-3.84) to use some of the EHR capabilities for both clinical care and organizational evaluation. We also found that hospitals that are part of a system were more likely (RRR, 2.73; 95% CI, 1.60-4.67) to use some of the EHR capabilities for clinical care and organizational evaluation than the hospitals that were not part of a system.

Discussion

Hospitals and hospital systems vary in the number of EHR systems that they use. Some apply a single-vendor EHR strategy (ie, the use of a single-vendor EHR throughout the hospital) to all the care they provide, whereas others use best-of-breed EHRs (ie, choosing components from different vendors to meet needs).20 These strategies create a mixture of outcomes associated with hospital operations, patient safety risks, and costs.1,21, 22, 23 This study examined the effect of hospital EHR use on hospital operations related to clinical care, organizational process, and both. In alignment with our hypothesis, we found that the hospitals using a single-vendor EHR system care are more likely to have used data from the EHR to support their clinical care evaluation (eg, QI process and monitor patient safety) and organizational management process (eg, measure organizational evaluation and inform strategic planning).

EHRs are real-time, patient-centered records that make information available instantly and securely to authorized users. They are built to share information with other health care providers, such as laboratories and specialists, so they contain information from all the clinicians involved in the patient’s care.24,25 Specifically, the ability to achieve interoperability can help hospitals provide better quality and safer care for patients.1 As interoperability increases, EHRs can also improve patient care by creating effective communication for information between parties.26 With a single-vendor interoperable EHR system, all hospital departments have ready access to the latest information allowing for a more coordinated, patient-centered care. The standardization of accessible and actionable data from the use of the single-vendor EHR system in a hospital will, in turn, likely increase efficiencies and cost savings for the hospital.23

However, it is difficult for clinicians and administrators to make decisions when the data on the patients are dispersed over multiple, noninteroperable EHR systems.27 A growing proportion of hospitals use single-vendor EHR systems for inpatient and outpatient services, including 73% of hospitals in this study. This single-vendor EHR strategy is likely to grow because of the potential efficiencies gained through standardization.6,8,23 As a result, it is not surprising that hospitals using the same vendor EHR system in both their inpatient and outpatient services are more likely to use resultant data to inform their clinical care decisions and organizational management process. Further, the greater use of EHR data because of a single-vendor EHR system in a hospital has been identified to improve hospital performance.8,23,28,29 It seems clear that hospitals can and will continue to leverage the EHR data to improve organizational performance. Moreover, those adopting a single-vendor EHR strategy have better capabilities to leverage both clinical and organizational data to inform clinical care and operational performance, likely because of improved interoperablity.1,30

Nevertheless, single-vendor systems are not without relevant problems. First, these systems may reduce customization and physician preferences.31,32 These limitations can contribute to inefficiency and promote problematic workarounds and potential safety errors,33 though other studies have indicated safety benefits associated with improved interoperability.34 Further, these systems can contribute to physician burnout because of a lack of flexibility and one-size-all approaches to documentation despite differences in practice and care patterns.35 Additionally, reliance on a single vendor could lead to monopolistic behavior by the vendor. Over time, organizations with single-vendor solutions may face increasing maintenance, subscription, and upgrade costs, potentially reducing the benefits gained from organizational and clinical performance improvements.6

One instance of the potential benefits of a single EHR is observed from the Veterans Health Affairs (VHA) hospitals, which were excluded from this analysis. The VHA system, although currently undergoing an EHR modernization, has been collecting EHRs for decades using a single system.36 It has also leveraged its single EHR system to assess and improve quality in various areas. For example, the VHA has leveraged surgical data to create a VHA Surgical Quality Improvement Program, which was subsequently used to develop the private sector version used by the American College of Surgeons.37 Similarly, nursing at the VHA has made use of big data to develop the VHA Nursing Outcomes Database to assess a range of variables, including demographic characteristics, financial, nursing-sensitive indicators, and hospital-acquired conditions.38,39 These data sets have promoted QI efforts throughout the VHA system as standardized data fields, and data collection allows for evaluation of quality over time. In turn, this provides an opportunity to compare and evaluate outcomes adequately.39, 40, 41, 42

Multiple hospital characteristics were also identified as influential in using the EHR data to inform clinical and organizational practice. For-profit hospitals were less likely to have used the EHR data for their clinical and organizational processes than nonfederal government hospitals. In contrast, nonprofit hospitals are more likely to use the EHR data for organizational processes. This finding builds on previous findings that support differences in the EHR use among hospital ownership types.43 Also, compared with independent hospitals, hospitals that belong to a system are more likely to have used electronic clinical data from the EHR in their clinical care and organizational management process. This is important as previous work has identified that hospitals that are part of a system see improvement in clinical care and operational performance compared with independent hospitals.44 The current work further suggests that leveraging clinical or organizational evaluation components derived from the EHR provides an opportunity for organizations to understand their current performance better and to leverage that information to meet their mission and improve outcomes.29

Finally, this study also reports more variance in hospital characteristics in using the EHR data for clinical care components such as QI process and patient safety than there are for organizational performance components. Although the EHR would be most easily leveraged to improve clinical care, the variation in the care components used paired with the lack of variation for organizational evaluation components indicates that hospitals either have an easier time using this data for organizational evaluation or are more inclined to do so because of financial benefits. Further, the EHR has been described by some as a billing instrument focused more on increasing documentation and less on the clinical outcomes of care.45 As such, organizational use of EHRs still appears to be driven mostly by process or volume instead of value, which may be reflected in the divergence of use for clinical care and the greater use of organizational evaluation components.

Limitation

There are several limitations to this study. First, this is a retrospective cross-sectional study relying on self-reported survey data. As such, the capabilities used may be inaccurately reported or could change over time. More specifically, we cannot determine changes in EHR usage, or if specific EHR components are developed over time. This is an important limitation as capabilities for EHR usage may mature over time despite single-vendor EHR strategies. However, although this may occur, it is, in our opinion, more likely that single-vendor systems provide a stronger likelihood for more robust EHR usage. Next, although we have assessed if the organization uses a single EHR vendor for inpatient and outpatient services, we have not accounted for vendor fragmentation. This is an important limitation as capabilities, and the ability to gather data from separate versions or separate modules within the same EHR vendor product may prohibit the use of clinical care or organizational evaluation components.

Conclusion

Hospitals continue to seek ways to leverage their EHR data in meaningful ways to impact clinical care, outcomes, and organizational performance. Although most hospitals used their EHR data, usage varied by hospitals with different EHR vendor systems. Hospitals using a single-vendor EHR system were more likely to engage in clinical care processes, organizational evaluation processes, and both. These processes include QI, patient safety, adherence to guidelines, performance profiles, and both unit and organization performance dashboards.

Potential Competing Interests

The authors report no competing interests.

Acknowledgments

Dr. Hamadi contributed to investigation, methodology, writing, original draft and reviewing, and editing. Dr. Niazi contributed to data curation, writing original draft, review and editing, and conceptualization. Dr. Zhao contributed to conceptualization, investigating, writing, review, and editing. Dr. Spaulding contributed to conceptualization, methodology, formal analysis, writing original draft, review and editing, and supervision.

Footnotes

Grant Support: This study was supported in part by Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery.

References

  • 1.The Office of the National Coordinator for Health Information Technology (ONC) Telemedicine and telehealth. HealthIT.gov. https://www.healthit.gov/topic/health-it-initiatives/telemedicine-and-telehealth
  • 2.Murphy D.R., Meyer A.N., Sittig D.F., Meeks D.W., Thomas E.J., Singh H. Application of electronic trigger tools to identify targets for improving diagnostic safety. BMJ Qual Saf. 2019;28(2):151–159. doi: 10.1136/bmjqs-2018-008086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Classen D., Li M., Miller S., Ladner D. An electronic health record-based real-time analytics program for patient safety surveillance and improvement. Health Aff (Millwood) 2018;37(11):1805–1812. doi: 10.1377/hlthaff.2018.0728. [DOI] [PubMed] [Google Scholar]
  • 4.Ford E.W., Menachemi N., Huerta T.R., Yu F. Hospital IT adoption strategies associated with implementation success: implications for achieving meaningful use. J Healthc Manag. 2010;55(3):175–188. doi: 10.1097/00115514-201005000-00007. [DOI] [PubMed] [Google Scholar]
  • 5.Barker W., Johnson C. The ecosystem of apps and software integrated with certified health information technology. J Am Med Inform Assoc. 2021;28(11):2379–2384. doi: 10.1093/jamia/ocab171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Koppel R., Lehmann C.U. Implications of an emerging EHR monoculture for hospitals and healthcare systems. J Am Med Inform Assoc. 2015;22(2):465–471. doi: 10.1136/amiajnl-2014-003023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Howe J.L., Adams K.T., Hettinger A.Z., Ratwani R.M. Electronic health record usability issues and potential contribution to patient harm. JAMA. 2018;319(12):1276–1278. doi: 10.1001/jama.2018.1171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Vest J.R., Unruh M.A., Freedman S., Simon K. Health systems’ use of enterprise health information exchange vs single electronic health record vendor environments and unplanned readmissions. J Am Med Inform Assoc. 2019;26(10):989–998. doi: 10.1093/jamia/ocz116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Miliard M. Healthcare IT News; 2020. Mayo Clinic completes Epic EHR rollout with final go-lives.https://www.healthcareitnews.com/news/mayo-clinic-completes-epic-ehr-rollout-final-go-lives [Google Scholar]
  • 10.Snyder B. How Kaiser bet $4 billion on electronic health records---and won. InfoWorld. https://www.infoworld.com/article/2614353/how-kaiser-bet--4-billion-on-electronic-health-records---and-won.html
  • 11.American Hospital Association The Go-To destination for reliable and consistent data about the nation’s hospitals. https://www.ahadata.com/why-aha-data
  • 12.Centers for Medicare and Medicaid Services Files for FY 2018 Final Rule and Correction Notice. Department of Health and Human Services. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Acute-Inpatient-Files-for-Download-Items/FY2018-Final-Rule-Correction-Notice-Files
  • 13.Sosunov E.A., Egorova N.N., Lin H.M., et al. The impact of hospital size on CMS hospital profiling. Med Care. 2016;54(4):373–379. doi: 10.1097/MLR.0000000000000476. [DOI] [PubMed] [Google Scholar]
  • 14.Hamadi H., Apatu E., Spaulding A. Does hospital ownership influence hospital referral region health rankings in the United States. Int J Health Plann Manage. 2018;33(1):e168–e180. doi: 10.1002/hpm.2442. [DOI] [PubMed] [Google Scholar]
  • 15.Chen A.S., Revere L., Ratanatawan A., Beck C.L., Allo J.A. A comparative analysis of academic and nonacademic hospitals on outcome measures and patient satisfaction. Am J Med Qual. 2019;34(4):367–375. doi: 10.1177/1062860618800586. [DOI] [PubMed] [Google Scholar]
  • 16.Bazzoli G.J., Chen H.F., Zhao M., Lindrooth R.C. Hospital financial condition and the quality of patient care. Health Econ. 2008;17(8):977–995. doi: 10.1002/hec.1311. [DOI] [PubMed] [Google Scholar]
  • 17.Wong H.S., Zhan C., Mutter R. Do different measures of hospital competition matter in empirical investigations of hospital behavior. Rev Ind Organ. 2005;26(1):27–60. [Google Scholar]
  • 18.Hamadi H., Spaulding A., Haley D.R., Zhao M., Tafili A., Zakari N. Does value-based purchasing affect US hospital utilization pattern: a comparative study. Int J Healthc Manag. 2019;12(2):148–154. [Google Scholar]
  • 19.Long J.S., Freese J. StataCorp LP; 2006. Regression models for categorical dependent variables using Stata. [Google Scholar]
  • 20.Payne T., Fellner J., Dugowson C., Liebovitz D., Fletcher G. Use of more than one electronic medical record system within a single health care organization. Appl Clin Inform. 2012;3(4):462–474. doi: 10.4338/ACI-2012-10-RA-0040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Russo E., Sittig D.F., Murphy D.R., Singh H. Challenges in patient safety improvement research in the era of electronic health records. Healthc (Amst) 2016;4(4):285–290. doi: 10.1016/j.hjdsi.2016.06.005. [DOI] [PubMed] [Google Scholar]
  • 22.Barnett M.L., Mehrotra A., Jena A.B. Adverse inpatient outcomes during the transition to a new electronic health record system: observational study. BMJ. 2016;354:i3835. doi: 10.1136/bmj.i3835. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Wang T., Gibbs D. A framework for performance comparison among major electronic health record systems. Perspect Health Inf Manag. 2019;16 (fall):1h. [PMC free article] [PubMed] [Google Scholar]
  • 24.Practice Fusion. EHR vs. EMR Definition, Benefits & EHR Usage Trends. Practice Fusion. https://www.practicefusion.com/blog/ehr-vs-emr/ Updated January 1, 2019.
  • 25.Anshari M. Redefining electronic health records (EHR) and electronic medical records (EMR) to promote patient empowerment. IJID. 2019;8(1):35–39. [Google Scholar]
  • 26.Kutney-Lee A., Sloane D.M., Bowles K.H., Burns L.R., Aiken L.H. Electronic health record adoption and nurse reports of usability and quality of care: the role of work environment. Appl Clin Inform. 2019;10(1):129–139. doi: 10.1055/s-0039-1678551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Shull J.G. Digital health and the state of interoperable electronic health records. JMIR Med Inform. 2019;7(4) doi: 10.2196/12712. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Adler-Milstein J., Everson J., Lee S.Y. EHR adoption and hospital performance: time-related effects. Health Serv Res. 2015;50(6):1751–1771. doi: 10.1111/1475-6773.12406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Adler-Milstein J., Holmgren A.J., Kralovec P., Worzala C., Searcy T., Patel V. Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide. J Am Med Inform Assoc. 2017;24(6):1142–1148. doi: 10.1093/jamia/ocx080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Sorace J., Wong H.H., DeLeire T., et al. Quantifying the competitiveness of the electronic health record market and its implications for interoperability. Int J Med Inform. 2020;136:104037. doi: 10.1016/j.ijmedinf.2019.104037. [DOI] [PubMed] [Google Scholar]
  • 31.Bentley T., Rizer M., McAlearney A.S., et al. The journey from precontemplation to action: transitioning between electronic medical record systems. Health Care Manage Rev. 2016;41(1):22–31. doi: 10.1097/HMR.0000000000000041. [DOI] [PubMed] [Google Scholar]
  • 32.Martinez N.M., Cortelyou-Ward K., George H.T., Arias J.J., Sompalli R. Best of breed electronic medical record comparative analysis. J Med Pract Manage. 2017;33(3):184–186. [Google Scholar]
  • 33.Ford E.W., Silvera G.A., Kazley A.S., Diana M.L., Huerta T.R. Assessing the relationship between patient safety culture and EHR strategy. Int J Health Care Qual Assur. 2016;29(6):614–627. doi: 10.1108/IJHCQA-10-2015-0125. [DOI] [PubMed] [Google Scholar]
  • 34.Bae J., Rask K.J., Becker E.R. The impact of electronic medical records on hospital-acquired adverse safety events: differential effects between single-source and multiple-source systems. Am J Med Qual. 2018;33(1):72–80. doi: 10.1177/1062860617702453. [DOI] [PubMed] [Google Scholar]
  • 35.Colicchio T.K., Cimino J.J., Del Fiol G. Unintended consequences of nationwide electronic health record adoption: challenges and opportunities in the post-meaningful use era. J Med Internet Res. 2019;21(6) doi: 10.2196/13313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Fihn S.D., Francis J., Clancy C., et al. Insights from advanced analytics at the Veterans Health Administration. Health Aff (Millwood) 2014;33(7):1203–1211. doi: 10.1377/hlthaff.2014.0054. [DOI] [PubMed] [Google Scholar]
  • 37.Massarweh N.N., Kaji A.H., Itani K.M.F. Practical guide to surgical data sets: Veterans Affairs Surgical Quality Improvement Program (VASQIP) JAMA Surg. 2018;153(8):768–769. doi: 10.1001/jamasurg.2018.0504. [DOI] [PubMed] [Google Scholar]
  • 38.Soban L.M., Kim L., Yuan A.H., Miltner R.S. Organisational strategies to implement hospital pressure ulcer prevention programmes: findings from a national survey. J Nurs Manag. 2017;25(6):457–467. doi: 10.1111/jonm.12416. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Deckro J., Phillips T., Davis A., Hehr A.T., Ochylski S. Big data in the veterans health administration: a nursing informatics perspective. J Nurs Scholarsh. 2021;53(3):288–295. doi: 10.1111/jnu.12631. [DOI] [PubMed] [Google Scholar]
  • 40.Chavez M.A., Duffy A., Rugs D., et al. Pressure injury documentation practices in the Department of Veteran Affairs: a quality improvement project. J Wound Ostomy Continence Nurs. 2019;46(1):18–24. doi: 10.1097/WON.0000000000000492. [DOI] [PubMed] [Google Scholar]
  • 41.Kutney-Lee A., Brennan C.W., Meterko M., Ersek M. Organization of nursing and quality of care for veterans at the end of life. J Pain Symptom Manage. 2015;49(3):570–577. doi: 10.1016/j.jpainsymman.2014.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Harolds J.A. Quality and safety in health care, part XVI: the VA Surgical Quality Improvement Program. Clin Nucl Med. 2016;41(11):862–863. doi: 10.1097/rlu.0000000000001359. [DOI] [PubMed] [Google Scholar]
  • 43.Yuan N., Dudley R.A., Boscardin W.J., Lin G.A. Electronic health records systems and hospital clinical performance: a study of nationwide hospital data. J Am Med Inform Assoc. 2019;26(10):999–1009. doi: 10.1093/jamia/ocz092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Spaulding A., Edwardson N. Centralizing value: an institutional theory perspective. Acad Manage. 2016:14164. [Google Scholar]
  • 45.Goroll A.H. Emerging from EHR purgatory—moving from process to outcomes. N Engl J Med. 2017;376(21):2004–2006. doi: 10.1056/NEJMp1700601. [DOI] [PubMed] [Google Scholar]

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