Disparities in Primary Care EHR Adoption Rates

Mack, Dominic; Zhang, Shun; Douglas, Megan; Sow, Charles; Strothers, Harry; Rust, George

doi:10.1353/hpu.2016.0016

Cited by 29 publications

(17 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Primary care physicians caring for Medicaid patients at risk for CVD were less likely to adopt fully functional CEHRT. The finding that PCPs treating Medicaid patients at risk for CVD were less likely than private insurance PCPs to adopt fully functional CEHRT is consistent with past analyses demonstrating that Medicaid providers had lower rates of EHR adoption compared to private insurance providers [25]. The variation in adoption of core EHR functionalities may exacerbate disparities in the provision of recommended preventive services for Medicaid patients at risk for CVD.…”

Section: Limitationssupporting

confidence: 81%

Impact of Core Electronic Health Record Functionalities on Cardiovascular Disease Preventive Health Services for Underserved Patients

K¹

2018

J Fam Med Dis Prev

View full text Add to dashboard Cite

Objectives: Examine differences in the associations between core Electronic Health Record (EHR) functionalities for public and population health, care coordination, patient engagement, and quality improvement on the provision of recommended Cardiovascular Disease (CVD) preventive health services provided at Primary Care Visits (PCVs) for CVD at-risk adults with Medicaid and those with private insurance. Methods: Utilizing a nationally representative survey of ambulatory care visits by patients at risk for CVD in 2014 and 2015, binary logistic regression models examined the associations between core EHR functionalities and the provision of recommended CVD preventive services for Medicaid and private insurance patients at risk for CVD, controlling for patient and provider confounding variables. Results: Primary care providers caring for Medicaid patients were significantly less likely to adopt fully functional certified EHR technology compared to PCPs caring for private insurance patients (75% vs. 82%, p < 0.01). Medicaid patients were significantly less likely to receive recommended blood pressure screening compared to private insurance patients (95% CI, 0.205-0.931). Among preventive visits for Medicaid patients at risk for CVD, significant associations were found between select recommended CVD preventive services and public and population health management, care coordination, and patient engagement. Discussion: Mixed results were found between core EHR functionalities and the provision of recommended CVD preventive services provided at PCVs for Medicaid patients. Low physician adoption of fully functional certified EHR technology may have impacted the provision of recommended CVD preventive services for Medicaid patients. Conclusion: Study findings demonstrate concerns about the rates of recommended CVD preventive services at PCVs for Medicaid patients at risk for CVD, which imply gaps in their care. Further investigation is required to understand how the approved list of electronic clinical quality measures impacts the associations between core EHR functionalities and the provision of recommended CVD preventive services delivered at PCVs.

show abstract

Section: Limitationssupporting

confidence: 81%

Impact of Core Electronic Health Record Functionalities on Cardiovascular Disease Preventive Health Services for Underserved Patients

K¹

2018

J Fam Med Dis Prev

View full text Add to dashboard Cite

show abstract

“…Due to their potential benefits, previous decades have witnessed rapid growth in the adoption of EHRs in health care settings. However, despite considerable investments by governments, the adoption of EHRs in some primary care organizations has been slow, especially in small practices [ 11 ], and disparities have been observed in terms of benefits associated with CISs in primary care practices [ 12 ]. In addition, other researchers have underscored that the potential benefits of EHRs are limited when health information stored in the system is shared only within the host institution, which means that greater benefits will be realized if only health information is shared beyond the host institution [ 13 , 14 ] and the technology used to support such sharing is health information exchange (HIE).…”

Section: Introductionmentioning

confidence: 99%

Adoption and Performance of Complementary Clinical Information Technologies: Analysis of a Survey of General Practitioners

Poba‐Nzaou¹,

Uwizeyemungu²,

Liu³

2020

J Med Internet Res

View full text Add to dashboard Cite

Background The benefits from the combination of 4 clinical information systems (CISs)—electronic health records (EHRs), health information exchange (HIE), personal health records (PHRs), and telehealth—in primary care depend on the configuration of their functional capabilities available to clinicians. However, our empirical knowledge of these configurations and their associated performance implications is very limited because they have mostly been studied in isolation. Objective This study aims to pursue 3 objectives: (1) characterize general practitioners (GPs) by uncovering the typical profiles of combinations of 4 major CIS capabilities, (2) identify physician and practice characteristics that predict cluster membership, and (3) assess the variation in the levels of performance associated with each configuration. Methods We used data from a survey of GPs conducted throughout the European Union (N=5793). First, 4 factors, that is, EHRs, HIE, PHRs, and Telehealth, were created. Second, a cluster analysis helps uncover clusters of GPs based on the 4 factors. Third, we compared the clusters according to five performance outcomes using an analysis of variance (ANOVA) and a Tamhane T2 post hoc test. Fourth, univariate and multivariate multinomial logistic regressions were used to identify predictors of the clusters. Finally, with a multivariate multinomial logistic regression, among the clusters, we compared performance in terms of the number of patients treated (3 levels) over the last 2 years. Results We unveiled 3 clusters of GPs with different levels of CIS capability profiles: strong (1956/5793, 37.36%), medium (2764/5793, 47.71%), and weak (524/5793, 9.04%). The logistic regression analysis indicates that physicians (younger, female, and less experienced) and practice (solo) characteristics are significantly associated with a weak profile. The ANOVAs revealed a strong cluster associated with significantly high practice performance outcomes in terms of the quality of care, efficiency, productivity, and improvement of working processes, and two noncomprehensive medium and weak profiles associated with medium (equifinal) practice performance outcomes. The logistic regression analysis also revealed that physicians in the weak profile are associated with a decrease in the number of patients treated over the last 2 years. Conclusions Different CIS capability profiles may lead to similar equifinal performance outcomes. This underlines the importance of looking beyond the adoption of 1 CIS capability versus a cluster of capabilities when studying CISs. GPs in the strong cluster exhibit a comprehensive CIS capability profile and outperform the other two clusters with noncomprehensive profiles, leading to significantly high performance in terms of the quality of care provided to patients, efficiency of the practice, productivity of the practice, and improvement of working processes. Our findings indicate that medical practices should develop high capabilities in all 4 CISs if they have to maximize their performance outcomes because efforts to develop high capabilities selectively may only be in vain.

show abstract

“…Integrating EHRs with CDS functions is likely to become a widespread trend [ 32 ]. Many countries have taken steps to develop relevant infrastructures and regulations and provide incentive policies to facilitate the adoption and integration of EHR systems [ 35 - 37 ]. However, few studies have discussed that the CPOE framework automatically invokes the centralized national EHR for decision support when physicians prescribe medications.…”

Section: Discussionmentioning

confidence: 99%

A Clinical Decision Support Engine Based on a National Medication Repository for the Detection of Potential Duplicate Medications: Design and Evaluation

Yang¹,

Lo²,

Chen³

et al. 2018

JMIR Med Inform

View full text Add to dashboard Cite

BackgroundA computerized physician order entry (CPOE) system combined with a clinical decision support system can reduce duplication of medications and thus adverse drug reactions. However, without infrastructure that supports patients’ integrated medication history across health care facilities nationwide, duplication of medication can still occur. In Taiwan, the National Health Insurance Administration has implemented a national medication repository and Web-based query system known as the PharmaCloud, which allows physicians to access their patients’ medication records prescribed by different health care facilities across Taiwan.ObjectiveThis study aimed to develop a scalable, flexible, and thematic design-based clinical decision support (CDS) engine, which integrates a national medication repository to support CPOE systems in the detection of potential duplication of medication across health care facilities, as well as to analyze its impact on clinical encounters.MethodsA CDS engine was developed that can download patients’ up-to-date medication history from the PharmaCloud and support a CPOE system in the detection of potential duplicate medications. When prescribing a medication order using the CPOE system, a physician receives an alert if there is a potential duplicate medication. To investigate the impact of the CDS engine on clinical encounters in outpatient services, a clinical encounter log was created to collect information about time, prescribed drugs, and physicians’ responses to handling the alerts for each encounter.ResultsThe CDS engine was installed in a teaching affiliate hospital, and the clinical encounter log collected information for 3 months, during which a total of 178,300 prescriptions were prescribed in the outpatient departments. In all, 43,844/178,300 (24.59%) patients signed the PharmaCloud consent form allowing their physicians to access their medication history in the PharmaCloud. The rate of duplicate medication was 5.83% (1843/31,614) of prescriptions. When prescribing using the CDS engine, the median encounter time was 4.3 (IQR 2.3-7.3) min, longer than that without using the CDS engine (median 3.6, IQR 2.0-6.3 min). From the physicians’ responses, we found that 42.06% (1908/4536) of the potential duplicate medications were recognized by the physicians and the medication orders were canceled.ConclusionsThe CDS engine could easily extend functions for detection of adverse drug reactions when more and more electronic health record systems are adopted. Moreover, the CDS engine can retrieve more updated and completed medication histories in the PharmaCloud, so it can have better performance for detection of duplicate medications. Although our CDS engine approach could enhance medication safety, it would make for a longer encounter time. This problem can be mitigated by careful evaluation of adopted solutions for implementation of the CDS engine. The successful key component of a CDS engine is the completeness of the patient’s medication history, thus further research to assess the f...

show abstract

Disparities in Primary Care EHR Adoption Rates

Cited by 29 publications

References 13 publications

Impact of Core Electronic Health Record Functionalities on Cardiovascular Disease Preventive Health Services for Underserved Patients

Impact of Core Electronic Health Record Functionalities on Cardiovascular Disease Preventive Health Services for Underserved Patients

Adoption and Performance of Complementary Clinical Information Technologies: Analysis of a Survey of General Practitioners

A Clinical Decision Support Engine Based on a National Medication Repository for the Detection of Potential Duplicate Medications: Design and Evaluation

Contact Info

Product

Resources

About