Using Electronic Health Records to Measure Quality Improvement Efforts: Findings from a Large Practice Facilitation Initiative

Augmenting project ECHO for opioid use disorder with data-informed quality improvement

BACKGROUND

National opioid-related overdose fatalities totaled 650,000 from 1999 to 2021. Some of the highest rates occurred in New Hampshire, where 40% of the population lives rurally. Medications for opioid use disorder (MOUD; methadone, buprenorphine, and naltrexone) have demonstrated effectiveness in reducing opioid overdose and mortality. Methadone access barriers disproportionally impact rural areas and naltrexone uptake has been limited. Buprenorphine availability has increased and relaxed regulations reduces barriers in general medical settings common in rural areas. Barriers to prescribing buprenorphine include lack of confidence, inadequate training, and lack of access to experts. To address these barriers, learning collaboratives have trained clinics on best-practice performance data collection to inform quality improvement (QI). This project sought to explore the feasibility of training clinics to collect performance data and initiate QI alongside clinics' participation in a Project ECHO virtual collaborative for buprenorphine providers.

METHODS

Eighteen New Hampshire clinics participating in a Project ECHO were offered a supplemental project exploring the feasibility of performance data collection to inform QI targeting increased alignment with best practice. Feasibility was assessed descriptively, through each clinic's participation in training sessions, data collection, and QI initiatives. An end-of-project survey was conducted to understand clinic staff perceptions of how useful and acceptable they found the program.

RESULTS

Five of the eighteen health care clinics that participated in the Project ECHO joined the training project, four of which served rural communities in New Hampshire. All five clinics met the criteria for engagement, as each clinic attended at least one training session, submitted at least one month of performance data, and completed at least one QI initiative. Survey results showed that while clinic staff perceived the training and data collection to be useful, there were several barriers to collecting the data, including lack of staff time, and difficulty standardizing documentation within the clinic electronic health record.

CONCLUSIONS

Results suggest that training clinics to monitor their performance and base QI initiatives on data has potential to impact clinical best practice. While data collection was inconsistent, clinics completed several data-informed QI initiatives, indicating that smaller scale data collection might be more attainable.

Barriers and facilitators to the implementation and adoption of improvement coaching: A qualitative evidence synthesis

BACKGROUND

Healthcare organisations and teams perform improvement activities to facilitate high-quality healthcare. The use of an improvement coach who provides support and guidance to the healthcare team may facilitate improvement activities; however, no systematic review exists on the facilitators and barriers to implementing an improvement coach.

AIMS

We conducted a qualitative evidence synthesis to examine the facilitators and barriers to the implementation of improvement coaching.

METHODS

We searched MEDLINE^® , Embase and CINAHL. The final search was in March 2021. The screening eligibility criteria included the following: interdisciplinary team receiving the coaching, improvement coaching, designs with a qualitative component and primary purpose of evaluating practice facilitation in OECD countries. An ecologically-informed consolidated framework for implementation research (CFIR) served as the framework for coding. Patterns of barriers and facilitators across domains were identified through matrix analysis. Risk of bias was assessed using Critical Appraisal Skills Program. PRISMA reporting guidelines served as a guide for reporting this review.

RESULTS

Nineteen studies with a qualitative component met the inclusion criteria. Four themes of barriers and facilitators crossed multiple CFIR domains: adaptability (e.g. making adjustments to the project; process, or approach); knowledge and skills (e.g. understanding of content and process for the project); engagement (e.g. willingness to be involved in the process) and resources (e.g. assets required to complete the improvement process).

CONCLUSION

Improvement coaching is a complex intervention that influences the context, healthcare team being coached and improvement activities. Improvement coaches should understand how to minimise barriers and promote facilitators that are unique to each improvement project across the domains. Limitations of the study are related to the nature of the intervention including potential publication bias given quality improvement focus; the variety of terms similar to improvement coaching or selection of framework.

Clinical data sharing improves quality measurement and patient safety

OBJECTIVE

Accurate and robust quality measurement is critical to the future of value-based care. Having incomplete information when calculating quality measures can cause inaccuracies in reported patient outcomes. This research examines how quality calculations vary when using data from an individual electronic health record (EHR) and longitudinal data from a health information exchange (HIE) operating as a multisource registry for quality measurement.

MATERIALS AND METHODS

Data were sampled from 53 healthcare organizations in 2018. Organizations represented both ambulatory care practices and health systems participating in the state of Kansas HIE. Fourteen ambulatory quality measures for 5300 patients were calculated using the data from an individual EHR source and contrasted to calculations when HIE data were added to locally recorded data.

RESULTS

A total of 79% of patients received care at more than 1 facility during the 2018 calendar year. A total of 12 994 applicable quality measure calculations were compared using data from the originating organization vs longitudinal data from the HIE. A total of 15% of all quality measure calculations changed (P < .001) when including HIE data sources, affecting 19% of patients. Changes in quality measure calculations were observed across measures and organizations.

DISCUSSION

These results demonstrate that quality measures calculated using single-site EHR data may be limited by incomplete information. Effective data sharing significantly changes quality calculations, which affect healthcare payments, patient safety, and care quality.

CONCLUSIONS

Federal, state, and commercial programs that use quality measurement as part of reimbursement could promote more accurate and representative quality measurement through methods that increase clinical data sharing.