Level of agreement on health information technology adoption and use in survey data: a mixed-methods analysis of ambulatory clinics in 1 US state

Barriers to using clinical decision support in ambulatory care: Do clinics in health systems fare better?

OBJECTIVE

We quantify the use of clinical decision support (CDS) and the specific barriers reported by ambulatory clinics and examine whether CDS utilization and barriers differed based on clinics' affiliation with health systems, providing a benchmark for future empirical research and policies related to this topic.

MATERIALS AND METHODS

Despite much discussion at the theoretic level, the existing literature provides little empirical understanding of barriers to using CDS in ambulatory care. We analyze data from 821 clinics in 117 medical groups, based on in Minnesota Community Measurement's annual Health Information Technology Survey (2014-2016). We examine clinics' use of 7 CDS tools, along with 7 barriers in 3 areas (resource, user acceptance, and technology). Employing linear probability models, we examine factors associated with CDS barriers.

RESULTS

Clinics in health systems used more CDS tools than did clinics not in systems (24 percentage points higher in automated reminders), but they also reported more barriers related to resources and user acceptance (26 percentage points higher in barriers to implementation and 33 points higher in disruptive alarms). Barriers related to workflow redesign increased in clinics affiliated with health systems (33 points higher). Rural clinics were more likely to report barriers to training.

CONCLUSIONS

CDS barriers related to resources and user acceptance remained substantial. Health systems, while being effective in promoting CDS tools, may need to provide further assistance to their affiliated ambulatory clinics to overcome barriers, especially the requirement to redesign workflow. Rural clinics may need more resources for training.

Assessment of Variation in Electronic Health Record Capabilities and Reported Clinical Quality Performance in Ambulatory Care Clinics, 2014-2017

IMPORTANCE

Electronic health records (EHRs) are widely promoted to improve the quality of health care, but information about the association of multifunctional EHRs with broad measures of quality in ambulatory settings is scarce.

OBJECTIVE

To assess the association between EHRs with different degrees of capabilities and publicly reported ambulatory quality measures in at least 3 clinical domains of care.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional and longitudinal study was conducted using survey responses from 1141 ambulatory clinics in Minnesota, Washington, and Wisconsin affiliated with a health system that responded to the Healthcare Information and Management Systems Society Annual Survey and reported performance measures in 2014 to 2017. Statistical analysis was performed from July 10, 2019, through February 26, 2021.

MAIN OUTCOMES AND MEASURES

A composite measure of EHR capability that considered 50 EHR capabilities in 7 functional domains, grouped into the following ordered categories: no functional EHR, EHR underuser, EHR, neither underuser or superuser, EHR superuser; as well as a standardized composite of ambulatory clinical performance measures that included 3 to 25 individual measures (median, 13 individual measures).

RESULTS

In 2014, 381 of 746 clinics (51%) were EHR superusers; this proportion increased in each subsequent year (457 of 846 clinics [54%] in 2015, 510 of 881 clinics [58%] in 2016, and 566 of 932 clinics [61%] in 2017). In each cross-sectional analysis year, EHR superusers had better clinical quality performance than other clinics (adjusted difference in score: 0.39 [95% CI, 0.12-0.65] in 2014; 0.29 [95% CI, -0.01 to 0.59] in 2015; 0.26 [95% CI, -0.05 to 0.56] in 2016; and 0.20 [95% CI, -0.04 to 0.45] in 2017). This difference in scores translates into an approximately 9% difference in a clinic's rank order in clinical quality. In longitudinal analyses, clinics that progressed to EHR superuser status had only slightly better gains in clinical quality between 2014 and 2017 compared with the gains in clinical quality of clinics that were static in terms of their EHR status (0.10 [95% CI, -0.13 to 0.32]). In an exploratory analysis, different types of EHR capability progressions had different degrees of associated improvements in ambulatory clinical quality (eg, progression from no functional EHR to a status short of superuser, 0.06 [95% CI, -0.40 to 0.52]; progression from EHR underuser to EHR superuser, 0.18 [95% CI, -0.14 to 0.50]).

CONCLUSIONS AND RELEVANCE

Between 2014 and 2017, ambulatory clinics in Minnesota, Washington, and Wisconsin with EHRs having greater capabilities had better composite measures of clinical quality than other clinics, but clinics that gained EHR capabilities during this time had smaller increases in clinical quality that were not statistically significant.

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.