Increasing Compliance With Documentation Using Modern Electronic Health Record Systems Toward Quality Improvement in Radiation Practice Medical Coding

BackgroundChronic obstructive pulmonary disease accounts for a significant portion of the world’s morbidity and mortality, and disproportionately affects low/middle-income countries. Chronic obstructive pulmonary disease management in low-resource settings is suboptimal with diagnostics, medications and high-quality, evidence-based care largely unavailable or unaffordable for most people. In early 2016, we aimed to improve the quality of chronic obstructive pulmonary disease management at Bayalpata Hospital in rural Achham, Nepal. Given that quality improvement infrastructure is limited in our setting, we also aimed to model the use of an electronic health record system for quality improvement, and to build local quality improvement capacity.DesignUsing international chronic obstructive pulmonary disease guidelines, the quality improvement team designed a locally adapted chronic obstructive pulmonary disease protocol which was subsequently converted into an electronic health record template. Over several Plan-Do-Study-Act cycles, the team rolled out a multifaceted intervention including educational sessions, reminders, as well as audits and feedback.ResultsThe rate of oral corticosteroid prescriptions for acute exacerbations of chronic obstructive pulmonary disease increased from 14% at baseline to >60% by month 7, with the mean monthly rate maintained above this level for the remainder of the initiative. The process measure of chronic obstructive pulmonary disease template completion rate increased from 44% at baseline to >60% by month 2 and remained between 50% and 70% for the remainder of the initiative.ConclusionThis case study demonstrates the feasibility of robust quality improvement programmes in rural settings and the essential role of capacity building in ensuring sustainability. It also highlights how individual quality improvement initiatives can catalyse systems-level improvements, which in turn create a stronger foundation for continuous quality improvement and healthcare system strengthening.

Download Full-text

The Surgical Risk Preoperative Assessment System: Determining which predictor variables can be automatically obtained from the electronic health record

Journal of Patient Safety and Risk Management ◽

10.1177/2516043519876489 ◽

2019 ◽

Vol 24 (6) ◽

pp. 230-237 ◽

Cited By ~ 1

Author(s):

Robert A Meguid ◽

Michael R Bronsert ◽

Karl E Hammermeister ◽

David P Kao ◽

Anne Lambert-Kerzner ◽

...

Keyword(s):

Quality Improvement ◽

Electronic Health Record ◽

Preoperative Assessment ◽

Assessment System ◽

Predictor Variables ◽

Health Record ◽

Improvement Program ◽

Surgical Quality ◽

Surgical Risk ◽

Electronic Health

Introduction The Surgical Risk Preoperative Assessment System is a parsimonious, universal surgical risk calculator integrated into our local electronic health record. We determined how many of its eight preoperative risk predictor variables could be automatically obtained from the electronic health record. This has implications for the usability and adoption of Surgical Risk Preoperative Assessment System, serving as an example of use of electronic health record data for populating clinical decision support tools. Methods We quantified the availability and accuracy in the electronic health record of the eight Surgical Risk Preoperative Assessment System predictor variables (patient age, American Society of Anesthesiology physical status classification, functional health status, sepsis, work Relative Value Unit, in-/outpatient operation, surgeon specialty, emergency status) at the patient’s preoperative encounter of 5205 patients entered into the American College of Surgeons National Surgical Quality Improvement Program. Accuracy was determined by comparing the electronic health record data to the same patient’s National Surgical Quality Improvement Program data, used as the “gold standard.” Acceptable accuracy was defined as a Kappa statistic or Pearson correlation coefficient ≥0.8 when comparing electronic health record and National Surgical Quality Improvement Program data. Acceptable availability was defined as presence of the variable in the electronic health record at the preoperative encounter ≥95% of the time. Results Of the eight predictor variables, six had acceptable accuracy. Only preoperative sepsis and functional health status had Kappa statistics <0.8. However, only patient age and surgeon specialty were ≥95% available in the electronic health record at the preoperative visit. Conclusions Processes need to be developed to populate more of the Surgical Risk Preoperative Assessment System preoperative predictor variables in the patient’s electronic health record prior to the preoperative visit to lessen the burden on the busy surgeon and encourage more widespread use of Surgical Risk Preoperative Assessment System.

Download Full-text

Using Electronic Health Record Data to Support Research and Quality Improvement: Practical Guidance from a Qualitative Investigation

ACI Open ◽

10.1055/s-0040-1713421 ◽

2020 ◽

Vol 04 (01) ◽

pp. e91-e101

Author(s):

Richard C. Wasserman ◽

Daria F. Ferro

Keyword(s):

Quality Improvement ◽

Electronic Health Record ◽

Health System ◽

System Level ◽

Health Record ◽

Learning Health System ◽

Level Ii ◽

Electronic Health ◽

Level I ◽

Support Research

Abstract Objective The aim of the study is to identify how academic health centers (AHCs) have established infrastructures to leverage electronic health record (EHR) data to support research and quality improvement (QI). Methods Phone interviews of 18 clinical informaticians with expertise gained over three decades at 24 AHCs were transcribed for qualitative analysis on three levels. In Level I, investigators independently used NVivo software to code and identify themes expressed in the transcripts. In Level II, investigators reexamined coded transcripts and notes and contextualized themes in the learning health system paradigm. In Level III, an informant subsample validated and supplemented findings. Results Level I analysis yielded six key “determinants”—Institutional Relationships, Resource Availability, Data Strategy, Response to Change, Leadership Support, and Degree of Mission Alignment—which, according to local context, affect use of EHR data for research and QI. Level II analysis contextualized these determinants in a practical frame of reference, yielding a model of learning health system maturation, over-arching key concepts, and self-assessment questions to guide AHC progress toward becoming a learning health system. Level III informants validated and supplemented findings. Discussion Drawn from the collective knowledge of experienced informatics professionals, the findings and tools described offer practical support to help clinical informaticians leverage EHR data for research and QI in AHCs. Conclusion The learning health system model builds on the tripartite AHC mission of research, education, and patient care. AHCs must deliberately transform into learning health systems to capitalize fully on EHR data as a staple of health learning.

Download Full-text

Leveraging the electronic health record for research and quality improvement: Current strengths and future challenges

Seminars in Colon and Rectal Surgery ◽

10.1053/j.scrs.2016.01.009 ◽

2016 ◽

Vol 27 (2) ◽

pp. 102-110 ◽

Cited By ~ 2

Author(s):

Elliot G. Arsoniadis ◽

Genevieve B. Melton

Keyword(s):

Quality Improvement ◽

Electronic Health Record ◽

Health Record ◽

Future Challenges ◽

Electronic Health

Download Full-text

The Intended and Unintended Consequences of Quality Improvement Interventions for Small Practices in a Community-based Electronic Health Record Implementation Project

Medical Care ◽

10.1097/mlr.0000000000000186 ◽

2014 ◽

Vol 52 (9) ◽

pp. 826-832 ◽

Cited By ~ 10

Author(s):

Andrew M. Ryan ◽

Colleen M. McCullough ◽

Sarah C. Shih ◽

Jason J. Wang ◽

Mandy S. Ryan ◽

...

Keyword(s):

Quality Improvement ◽

Electronic Health Record ◽

Unintended Consequences ◽

Health Record ◽

Community Based ◽

Implementation Project ◽

Improvement Interventions ◽

Electronic Health ◽

Quality Improvement Interventions

Download Full-text

Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocw083 ◽

2016 ◽

Vol 24 (2) ◽

pp. 288-294 ◽

Cited By ~ 1

Author(s):

Gayle Shier Kricke ◽

Matthew B Carson ◽

Young Ji Lee ◽

Corrine Benacka ◽

R. Kannan Mutharasan ◽

...

Keyword(s):

Quality Improvement ◽

Electronic Health Record ◽

Failure Mode ◽

Daily Practice ◽

Risk Process ◽

Health Record ◽

Process Map ◽

Process Maps ◽

Diagnostic Measures ◽

Electronic Health

Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning. Methods: Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation. Results: EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation. Conclusion: Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.

Download Full-text