Area-level factors associated with electronic health record adoption and meaningful use in the Regional Extension Center Program

The Impact of Market Factors on Meaningful Use of Electronic Health Records Among Primary Care Providers: Evidence From Florida Using Resource Dependence Theory and Information Uncertainty Perspective

BACKGROUND

Using federal funds from the 2009 Health Information Technology for Economic and Clinical Health Act, the Centers for Medicare and Medicaid Services funded the 2011-2021 Medicaid electronic health record (EHR) incentive programs throughout the country.

OBJECTIVE

Identify the market factors associated with Meaningful Use (MU) of EHRs after primary care providers (PCPs) enrolled in the Florida-EHR incentives program through Adopting, Improving, or Upgrading (AIU) an EHR technology.

RESEARCH DESIGN

Retrospective cohort study using 2011-2018 program records for 8464 Medicaid providers.

MAIN OUTCOME

MU achievement after first-year incentives.

INDEPENDENT VARIABLES

The resource dependence theory and the information uncertainty perspective were used to generate key-independent variables, including the county's rurality, educational attainment, poverty, health maintenance organization penetration, and number of PCPs per capita.

ANALYTICAL APPROACH

All the county rates were converted into 3 dichotomous measures corresponding to high, medium, and low terciles. Descriptive and bivariate statistics were calculated. A generalized hierarchical linear model was used because MU data were clustered at the county level (level 2) and measured at the practice level (level 1).

RESULTS

Overall, 41.9% of Florida Medicaid providers achieved MU after receiving first-year incentives. Rurality was positively associated with MU ( P <0.001). Significant differences in MU achievements were obtained when we compared the "high" terciles with the "low" terciles for poverty rates ( P =0.002), health maintenance organization penetration rates ( P =0.02), and number of PCPs per capita ( P =0.01). These relationships were negative.

CONCLUSIONS

Policy makers and health care managers should not ignore the contribution of market factors in EHR adoption.

The impact of county-level factors on meaningful use of electronic health records (EHRs) among primary care providers

This study examines the impact of county-level factors on "meaningful use" (MU) of electronic health records (EHRs) for 8415 primary care providers (PCPs) that enrolled in the Florida Medicaid EHR Incentive Program through adopting, improving, or upgrading (AIU) a certified EHR technology. PCPs received incentive payments at enrollment and if they used their EHRs in meaningful ways; ways that benefit patients and providers alike they received additional payments. We conducted a retrospective cohort study of these providers over the 2011-2018 period while linking their records to other state data. We used the core constructs of the resource dependence theory (RDT), a well-established organization theory in business management, to operationalize the county-level variables. These variables were rurality, poverty, educational attainment, managed care penetration, changes in population, and number of PCPs per capita. The unit of analysis was provider-years. For practical and computational purposes, all the county variables were dichotomized. We used analysis of variance (ANOVA) to test for differences in MU attestation rates across each county variable. Odds ratios and corresponding 95% confidence intervals were derived from pooled logistic regressions using generalized estimated equations (GEE) with the binomial family and logit link functions. Clustered standard errors were used. Approximately 42% of these providers attested to MU after receiving first-year incentives. Rurality and poverty were significantly associated with MU. To some degree, managed care penetration, change in population size, and number of PCPs per capita were also associated with MU. Policy makers and healthcare managers should not ignore the contribution of county-level factors in the diffusion of EHRs among physician practices. These county-level findings provide important insights about EHR diffusion in places where traditionally underserved populations live. This county-perspective is particularly important because of the potential for health IT to enable public health monitoring and population health management that might benefit individuals beyond the patients treated by the Medicaid providers.

Use of electronic health records to manage tobacco screening and treatment in rural primary care

PURPOSE

Electronic health records (EHRs) can facilitate primary care providers' (PCPs) use of best practices in addressing tobacco dependence. It is unknown whether rural PCPs reap the same benefits as their urban counterparts when employing EHRs for this purpose. Our study examines this issue.

METHODS

This cross-sectional investigation based on the 2012-2015 National Ambulatory Medical Care Survey used chi-square tests and adjusted logistic regression models to explore how rurality and use of tobacco-related EHR functions were related to smoking status documentation (SSD) and cessation treatment at adult primary care visits.

FINDINGS

SSD rates were similar in visits to rural- and urban-based PCPs (88.2% rural-based vs 81.1% urban-based, P = .5819). Use of EHRs for SSD was associated with higher SSD odds at visits to both rural- and urban-based PCPs, but this increase was greater for visits to rural-based PCPs (428% vs 220% urban-based, P = .0443). Rates of cessation treatment at smokers' visits were low in rural and urban contexts (19.3% rural vs 19.6% urban, P = .9430). Odds of cessation treatment were 68% higher where EHRs were used to remind PCPs of treatment guidelines (P = .001), with no rural-urban difference in the size of the increase. Access to EHRs with tobacco-related functions was similar across rural and urban practices.

CONCLUSIONS

Rural-based PCPs were at least as successful as urban-based PCPs in leveraging EHRs to enhance tobacco-related services. Even where EHRs are used, opportunities exist to expand cessation treatment in rural primary care.

Provider participation in the Florida Medicaid Promoting Interoperability program: Practice characteristics, meaning use attestations, and incentive payments

OBJECTIVE

The objective of this study was to quantify the rate of provider participation beyond year 1 incentive in the Florida Medicaid Promoting Interoperability (PI) program, formerly the Electronic Health Record Incentive program, and identify the provider and practice characteristics associated with Meaningful Use attestations.

METHODS AND MATERIALS

We conducted a retrospective cohort study using the 2011-2018 records from the PI program, Provider Participation Database. Bivariate associations between Meaningful Use and categorical and ordinal variables were tested using Chi-square and Mantel-Haenszel Chi-square, respectively, with results informing logistic regressions. Adjusted odds ratios and 95 % confidence intervals are reported.

RESULTS

We found that 42.56 % of Florida Medicaid providers achieved Meaningful Use after receiving first-year incentives. Logistic regression showed that pediatricians represented the largest percentage of providers who achieved Meaningful Use (65.06 %) while dentists had the lowest Meaningful Use (7.78 %). We also found that certain geographic areas and various EHR vendors were associated with higher rates of providers Meaningful Use attestation.

DISCUSSION

Although the PI program successfully influenced the adoption of a basic EHR system, low Meaningful Use attestations have inadvertently created a digital "advanced use" divide among providers who serve large numbers of Medicaid patients. This is concerning because advanced EHR functions are necessary precursors to address unmet socioeconomic needs to reduce health disparities.

CONCLUSION

Florida has distributed over $100 million to Medicaid providers who ultimately did not achieve Meaningful Use after collecting their first-year incentive. Policy interventions that can promote advanced EHR use functions are necessary to optimize technology in low-resourced practice settings where the potential benefits are greater.

Optimizing health IT to improve health system performance: A work in progress

BACKGROUND

Despite significant investments in health information technology (IT), the technology has not yielded the intended performance effects or transformational change. We describe activities that health systems are pursuing to better leverage health IT to improve performance.

METHODS

We conducted semi-structured telephone interviews with C-suite executives from 24 U.S. health systems in four states during 2017-2019 and analyzed the data using a qualitative thematic approach.

RESULTS

Health systems reported two broad categories of activities: laying the foundation to improve performance with IT and using IT to improve performance. Within these categories, health systems were engaged in similar activities but varied greatly in their progress. The most substantial effort was devoted to the first category, which enabled rather than directly improved performance, and included consolidating to a single electronic health record (EHR) platform and common data across the health system, standardizing data elements, and standardizing care processes before using the EHR to implement them. Only after accomplishing such foundational activities were health systems able to focus on using the technology to improve performance through activities such as using data and analytics to monitor and provide feedback, improving uptake of evidence-based medicine, addressing variation and overuse, improving system-wide prevention and population health management, and making care more convenient.

CONCLUSIONS AND IMPLICATIONS

Leveraging IT to improve performance requires significant and sustained effort by health systems, in addition to significant investments in hardware and software. To accelerate change, better mechanisms for creating and disseminating best practices and providing advanced technical assistance are needed.

Growing Disparities in Patient-Provider Messaging: Trend Analysis Before and After Supportive Policy

BACKGROUND

Public policy introduced since 2011 has supported provider adoption of electronic medical records (EMRs) and patient-provider messaging, primarily through financial incentives. It is unclear how disparities in patients' use of incentivized electronic health (eHealth) tools, like patient-provider messaging, have changed over time relative to disparities in use of eHealth tools that were not directly incentivized.

OBJECTIVE

This study examines trends in eHealth disparities before and after the introduction of US federal financial incentives. We compare rates of patient-provider messaging, which was directly incentivized, with rates of looking for health information on the Web, which was not directly incentivized.

METHODS

We used nationally representative Health Information National Trends Survey data from 2003 to 2018 (N=37,300) to describe disparities in patient-provider messaging and looking for health information on the Web. We first reported the percentage of individuals across education and racial and ethnic groups who reported using these tools in each survey year and compared changes in unadjusted disparities during preincentive (2003-2011) and postincentive (2011-2018) periods. Using multivariable linear probability models, we then examined adjusted effects of education and race and ethnicity in 3 periods-preincentive (2003-2005), early incentive (2011-2013), and postincentive (2017-2018)-controlling for sociodemographic and health factors. In the postincentive period, an additional model tested whether internet adoption, provider access, or providers' use of EMRs explained disparities.

RESULTS

From 2003 to 2018, overall rates of provider messaging increased from 4% to 36%. The gap in provider messaging between the highest and lowest education groups increased by 10 percentage points preincentive (P<.001) and 22 additional points postincentive (P<.001). The gap between Hispanics and non-Hispanic whites increased by 3.2 points preincentive (P=.42) and 11 additional points postincentive (P=.01). Trends for blacks resembled those for Hispanics, whereas trends for Asians resembled those for non-Hispanic whites. In contrast, education-based disparities in looking for health information on the Web (which was not directly incentivized) did not significantly change in preincentive or postincentive periods, whereas racial disparities narrowed by 15 percentage points preincentive (P=.008) and did not significantly change postincentive. After adjusting for other sociodemographic and health factors, observed associations were similar to unadjusted associations, though smaller in magnitude. Including internet adoption, provider access, and providers' use of EMRs in the postincentive model attenuated, but did not eliminate, education-based disparities in provider messaging and looking for health information on the Web. Racial and ethnic disparities were no longer statistically significant in adjusted models.

CONCLUSIONS

Disparities in provider messaging widened over time, particularly following federal financial incentives. Meanwhile, disparities in looking for health information on the Web remained stable or narrowed. Incentives may have disproportionately benefited socioeconomically advantaged groups. Future policy could address disparities by incentivizing providers treating these populations to adopt messaging capabilities and encouraging patients' use of messaging.

Facility and Regional Factors Associated With the New Adoption of Electronic Medical Records in Japan: Nationwide Longitudinal Observational Study

Background

The rate of adoption of electronic medical record (EMR) systems has increased internationally, and new EMR adoption is currently a major topic in Japan. However, no study has performed a detailed analysis of longitudinal data to evaluate the changes in the EMR adoption status over time.

Objective

This study aimed to evaluate the changes in the EMR adoption status over time in hospitals and clinics in Japan and to examine the facility and regional factors associated with these changes.

Methods

Secondary longitudinal data were created by matching data in fiscal year (FY) 2011 and FY 2014 using reference numbers. EMR adoption status was defined as “EMR adoption,” “specified adoption schedule,” or “no adoption schedule.” Data were obtained for hospitals (n=4410) and clinics (n=67,329) that had no adoption schedule in FY 2011 and for hospitals (n=1068) and clinics (n=3132) with a specified adoption schedule in FY 2011. The EMR adoption statuses of medical institutions in FY 2014 were also examined. A multinomial logistic model was used to investigate the associations between EMR adoption status in FY 2014 and facility and regional factors in FY 2011. Considering the regional variations of these models, multilevel analyses with second levels were conducted. These models were constructed separately for hospitals and clinics, resulting in four multinomial logistic models. The odds ratio (OR) and 95% Bayesian credible interval (CI) were estimated for each variable.

Results

A total of 6.9% of hospitals and 14.82% of clinics with no EMR adoption schedules in FY 2011 had adopted EMR by FY 2014, while 10.49% of hospitals and 33.65% of clinics with specified adoption schedules in FY 2011 had cancelled the scheduled adoption by FY 2014. For hospitals with no adoption schedules in FY 2011, EMR adoption/scheduled adoption was associated with practice size characteristics, such as number of outpatients (from quantile 4 to quantile 1: OR 1.67, 95% CI 1.005-2.84 and OR 2.40, 95% CI 1.80-3.21, respectively), and number of doctors (from quantile 4 to quantile 1: OR 4.20, 95% CI 2.39-7.31 and OR 2.02, 95% CI 1.52-2.64, respectively). For clinics with specified EMR adoption schedules in FY 2011, the factors negatively associated with EMR adoption/cancellation of scheduled EMR adoption were the presence of beds (quantile 4 to quantile 1: OR 0.57, 95% CI 0.45-0.72 and OR 0.74, 95% CI 0.58-0.96, respectively) and having a private establisher (quantile 4 to quantile 1: OR 0.27, 95% CI 0.13-0.55 and OR 0.43, 95% CI 0.19-0.91, respectively). No regional factors were significantly associated with the EMR adoption status of hospitals with no EMR adoption schedules; population density was positively associated with EMR adoption in clinics with no EMR adoption schedule (quantile 4 to quantile 1: OR 1.49, 95% CI 1.32-1.69).

Conclusions

Different approaches are needed to promote new adoption of EMR systems in hospitals as compared to clinics. It is important to induce decision making in small- and medium-sized hospitals, and regional postdecision technical support is important to avoid cancellation of scheduled EMR adoption in clinics.

Exploring Factors Affecting Voluntary Adoption of Electronic Medical Records Among Physicians and Clinical Assistants of Small or Solo Private General Practice Clinics

The health care reform initiative led by the Hong Kong government's Food and Health Bureau has started the implementation of an electronic sharing platform to provide an information infrastructure that enables public hospitals and private clinics to share their electronic medical records (EMRs) for improved access to patients' health care information. However, previous attempts to convince the private clinics to adopt EMRs to document health information have faced challenges, as the EMR adoption has been voluntary. The lack of electronic data shared by private clinics carries direct impacts to the efficacy of electronic record sharing between public and private healthcare providers. To increase the likelihood of buy-in, it is essential to proactively identify the users' and organizations' needs and capabilities before large-scale implementation. As part of the reform initiative, this study examined factors affecting the adoption of EMRs in small or solo private general practice clinics, by analyzing the experiences and opinions of the physicians and clinical assistants during the pilot implementation of the technology, with the purpose to learn from it before full-scale rollout. In-depth, semistructured interviews were conducted with 23 physicians and clinical assistants from seven small or solo private general practice clinics to evaluate their experiences, expectations, and opinions regarding the deployment of EMRs. Interview transcripts were content analyzed to identify key factors. Factors affecting the adoption of EMRs to record and manage health care information were identified as follows: system interface design; system functions; stability and reliability of hardware, software, and computing networks; financial and time costs; task and outcome performance, work practice, and clinical workflow; physical space in clinics; trust in technology; users' information technology literacy; training and technical support; and social and organizational influences. The factors are interrelated with the others. The adoption factors identified are multifaceted, ranging from technological characteristics, clinician-technology interactions, skills and knowledge, and the user-workflow-technology fit. Other findings, which have been relatively underrepresented in previous studies, contribute unique insights about the influence of work and social environment on the adoption of EMRs, including limited clinic space and the effects of physicians' decision to use the technology on clinical staffs' adoption decisions. Potential strategies to address the concerns, overcome adoption barriers, and define relevant policies are discussed.

Regional differences in electronic medical record adoption in Japan: A nationwide longitudinal ecological study

PURPOSE

Regional differences in the adoption of electronic medical records (EMR) are a major problem, yet little is known about these differences internationally. We analyzed regional differences in EMR adoption in Japan and evaluated factors associated with these differences.

METHODS

This nationwide ecological study used secondary data from all secondary medical service areas (SMSAs) in fiscal years 2008 (n = 348) and 2014 (n = 344). For each SMSA we collected the following information from a Japanese national database: the number of medical facilities that had adopted EMR, the population density, the average per capita income, the number of working doctors per 1000 people, and the proportion of interns to all working doctors. To adjust for medical facility characteristics in each SMSA, such as number of beds, public versus private hospital, and hospital type (psychiatric or other), we estimated the standardized adoption ratio (SAR) for EMR adoption, modeled on the standardized mortality ratio. We calculated Moran's I for the SAR and investigated whether the SAR had spatial autocorrelations. We evaluated the association between the SAR and regional factors with a conditional autoregressive model. We compared these results in 2008 and 2014, for both hospitals and clinics.

RESULTS

While the EMR adoption rate in SMSAs increased, Moran's I of the SAR in hospitals was close to 1 in both 2008 and 2014, and Moran's I of the SAR in clinics increased from 2008 to 2014. For hospitals, there was a significant association between the proportion of interns to all working doctors and the SAR only in 2008. For clinics, average income in the SMSA was positively associated with the SAR, whereas the number of working doctors was negatively associated with the SAR in both 2008 and 2014. Population density was positively associated with the SAR only in 2014.

CONCLUSION

From 2008 to 2014, EMR adoption in Japan generally increased, but geographical differences did not improve. Regional factors associated with the SAR were different for hospitals than for clinics. Therefore, the government should take different approaches for clinics and hospitals to improve regional differences in EMR adoption, especially in providing financial and technical support.

How can hospitals better protect the privacy of electronic medical records? Perspectives from staff members of health information management departments

PURPOSE

The adoption of electronic medical records (EMR) is expected to better improve overall healthcare quality and to offset the financial pressure of excessive administrative burden. However, safeguarding EMR against potentially hostile security breaches from both inside and outside healthcare facilities has created increased patients' privacy concerns from all sides. The aim of our study was to examine the influencing factors of privacy protection for EMR by healthcare professionals.

METHOD

We used survey methodology to collect questionnaire responses from staff members in health information management departments among nine Taiwanese hospitals active in EMR utilisation. A total of 209 valid responses were collected in 2014. We used partial least squares for analysing the collected data.

RESULTS

Perceived benefits, perceived barriers, self-efficacy and cues to action were found to have a significant association with intention to protect EMR privacy, while perceived susceptibility and perceived severity were not.

CONCLUSION

Based on the findings obtained, we suggest that hospitals should provide continuous ethics awareness training to relevant staff and design more effective strategies for improving the protection of EMR privacy in their charge. Further practical and research implications are also discussed.

Adoption Factors of the Electronic Health Record: A Systematic Review

Background

The Health Information Technology for Economic and Clinical Health (HITECH) was a significant piece of legislation in America that served as a catalyst for the adoption of health information technology. Following implementation of the HITECH Act, Health Information Technology (HIT) experienced broad adoption of Electronic Health Records (EHR), despite skepticism exhibited by many providers for the transition to an electronic system. A thorough review of EHR adoption facilitator and barriers provides ongoing support for the continuation of EHR implementation across various health care structures, possibly leading to a reduction in associated economic expenditures.

Objective

The purpose of this review is to compile a current and comprehensive list of facilitators and barriers to the adoption of the EHR in the United States.

Methods

Authors searched Cumulative Index of Nursing and Allied Health Literature (CINAHL) and MEDLINE, 01/01/2012–09/01/2015, core clinical/academic journals, MEDLINE full text, and evaluated only articles germane to our research objective. Team members selected a final list of articles through consensus meetings (n=31). Multiple research team members thoroughly read each article to confirm applicability and study conclusions, thereby increasing validity.

Results

Group members identified common facilitators and barriers associated with the EHR adoption process. In total, 25 adoption facilitators were identified in the literature occurring 109 times; the majority of which were efficiency, hospital size, quality, access to data, perceived value, and ability to transfer information. A total of 23 barriers to adoption were identified in the literature, appearing 95 times; the majority of which were cost, time consuming, perception of uselessness, transition of data, facility location, and implementation issues.

Conclusions

The 25 facilitators and 23 barriers to the adoption of the EHR continue to reveal a preoccupation on cost, despite incentives in the HITECH Act. Limited financial backing and outdated technology were also common barriers frequently mentioned during data review. Future public policy should include incentives commensurate with those in the HITECH Act to maintain strong adoption rates.

National Trends in Nonoperative Management of Rectal Adenocarcinoma

Purpose

Neoadjuvant chemoradiation for stage II/III rectal cancer results in up to 49% of patients with a clinical complete response. As a result, many have questioned whether surgery can be omitted for this group of patients. Currently, there is insufficient evidence for chemoradiation only, or nonoperative management (NOM), to support its adoption. Despite this, anecdotal evidence suggests there is a trend toward increased use of NOM. Our objective was to examine the use of NOM for rectal cancer over time, as well as the patient- and facility-level factors associated with its use.

Methods

We included all incident cases of invasive, nonmetastatic rectal adenocarcinoma reported to the National Cancer Database from 1998 to 2010. We performed univariate and multivariate analyses to assess for NOM use over time, as well as associated patient- and facility-level factors.

Results

A total of 146,135 patients met the inclusion criteria: 5,741 had NOM and 140,394 had surgery with or without additional therapy. From 1998 to 2010, NOM doubled, from 2.4% to 5% of all cases annually. Patients who were black (adjusted odds ratio [AOR], 1.71; 95% CI, 1.57 to 1.86), uninsured (AOR, 2.35; 95% CI, 2.08 to 2.65) or enrolled in Medicaid (AOR, 2.10; 95% CI, 1.90 to 2.33), or treated at low-volume facilities (AOR, 1.53; 95% CI, 1.42 to 1.64) were more likely to receive NOM than were patients who were white, privately insured, and treated at a high-volume facility, respectively.

Conclusion

NOM demonstrates promise for the treatment of rectal cancer; currently, however, the most appropriate strategy is to pursue this approach with well-informed patients in the context of a clinical trial. We observed evidence of increasing NOM use, with this increase occurring more frequently in black and uninsured/Medicaid patients, raising concern that increased NOM use may actually represent increasing disparities in rectal cancer care rather than innovation. Further studies are needed to assess survival differences by treatment strategy.

Impact of the HITECH Act on physicians' adoption of electronic health records

BACKGROUND AND OBJECTIVE

The Health Information Technology for Economic and Clinical Health (HITECH) Act has distributed billions of dollars to physicians as incentives for adopting certified electronic health records (EHRs) through the meaningful use (MU) program ultimately aimed at improving healthcare outcomes. The authors examine the extent to which the MU program impacted the EHR adoption curve that existed prior to the Act.

METHODS

Bass and Gamma Shifted Gompertz (G/SG) diffusion models of the adoption of "Any" and "Basic" EHR systems in physicians' offices using consistent data series covering 2001-2013 and 2006-2013, respectively, are estimated to determine if adoption was stimulated during either a PrePay (2009-2010) period of subsidy anticipation or a PostPay (2011-2013) period when payments were actually made.

RESULTS

Adoption of Any EHR system may have increased by as much as 7 percentage points above the level predicted in the absence of the MU subsidies. This estimate, however, lacks statistical significance and becomes smaller or negative under alternative model specifications. No substantial effects are found for Basic systems. The models suggest that adoption was largely driven by "imitation" effects (q-coefficient) as physicians mimic their peers' technology use or respond to mandates. Small and often insignificant "innovation" effects (p-coefficient) are found suggesting little enthusiasm by physicians who are leaders in technology adoption.

CONCLUSION

The authors find weak evidence of the impact of the MU program on EHR uptake. This is consistent with reports that many current EHR systems reduce physician productivity, lack data sharing capabilities, and need to incorporate other key interoperability features (e.g., application program interfaces).