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

2016 ◽  
Vol 24 (2) ◽  
pp. 288-294 ◽  
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.

Abstract 215: Leveraging Electronic Health Record Documentation for Failure Mode and Effects Analysis Team Identification on an Inpatient Cardiology Unit

2016 ◽  
Vol 9 (suppl_2) ◽  
Author(s):  
Gayle S Kricke ◽  
Matthew Carson ◽  
Young Ji Lee ◽  
Corrine Benacka ◽  
Faraz Ahmad ◽  
...  
Keyword(s):  
Quality Improvement ◽  
High Risk ◽  
Electronic Health Record ◽  
Failure Mode ◽  
Planning Process ◽  
Discharge Planning ◽  
Health Record ◽  
Discharge Process ◽  
Electronic Health ◽  
Risk Processes

Objectives: Failure Mode and Effects Analysis (FMEA) is a frequently-used approach for prospective risk assessment and quality improvement in healthcare, particularly for high-risk care processes such as hospital discharge planning. Our goal was to evaluate whether secondary use of metadata collected by the electronic health record (EHR) during daily practice can inform assembly of a comprehensive FMEA team by showing: 1) discrepancies between expected and observed process activities and individuals involved, and 2) the presence of individuals who may be appropriate to include in an FMEA based on their variable familiarity with a process. Methods: We extracted discharge planning data for an inpatient cardiology unit from the Enterprise Data Warehouse (EDW) and compared it to a hand-drawn map (HDM) indicating clinicians’ understanding of discharge activities and providers expected to complete each activity. We assessed the presence of providers highly experienced in the process, the diversity of involved disciplines, and the accuracy of the HDM compared to observation from EDW data. Findings: Over 500 providers completed nearly 35,000 discharge-related activities across 18 activity types over 2,000 encounters. Experience was skewed such that 90% (510 of 569) of providers completed between 0 and 99 activities while the remaining 10% (59 of 569) performed up to 1,200 activities. Frequent performers completed similar activities to their peers, but did so as many as 12 times more frequently than average for their discipline. Expectation of who performed an activity closely matched observation for 11 discipline-specific activities, such as case management assessment. However, providers from up to 10 different disciplines performed the remaining 7 activities, such as scheduling a follow-up visit or ordering a therapy consult. Overall, 35% (12,183 of 34,939) of activities were performed by an unexpected provider. Conclusions: Analyzing metadata from EHRs is a novel method to inform FMEA of high-risk processes. This study provides a framework for assessing process activities and the providers involved. In the discharge planning process, there appears to be significant discrepancy between clinicians’ understanding and the actual discharge process and team, which suggests the presence of providers who could be overlooked during typical FMEA team construction. This methodology can empirically enrich the FMEA team and highlight quality improvement target areas.

scholarly journals Electronic Health Record Acceptance by Physicians: A Single Hospital Experience in Daily Practice

2021 ◽  
Vol 1 (1) ◽  
pp. 6-17
Author(s):  
Andrija Pavlovic ◽  
Nina Rajovic ◽  
Jasmina Pavlovic Stojanovic ◽  
Debora Akinyombo ◽  
Milica Ugljesic ◽  
...  
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Electronic Health Record ◽  
Technology Acceptance ◽  
Daily Practice ◽  
Ease Of Use ◽  
Future Research ◽  
Health Record ◽  
Cross Sectional ◽  
Electronic Health

Introduction: Potential benefits of implementing an electronic health record (EHR) to increase the efficiency of health services and improve the quality of health care are often obstructed by the unwillingness of the users themselves to accept and use the available systems. Aim: The aim of this study was to identify factors that influence the acceptance of the use of an EHR by physicians in the daily practice of hospital health care. Material and Methods: The cross-sectional study was conducted among physicians in the General Hospital Pancevo, Serbia. An anonymous questionnaire, developed according to the technology acceptance model (TAM), was used for the assessment of EHR acceptance. The response rate was 91%. Internal consistency was assessed by Cronbach’s alpha coefficient. A logistic regression analysis was used to identify the factors influencing the acceptance of the use of EHR. Results: The study population included 156 physicians. The mean age was 46.4 ± 10.4 years, 58.8% participants were female. Half of the respondents (50.1%) supported the use of EHR in comparison to paper patient records. In multivariate logistic regression modeling of social and technical factors, ease of use, usefulness, and attitudes towards use of EHR as determinants of the EHR acceptance, the following predictors were identified: use of a computer outside of the office for reading daily newspapers (p = 0.005), EHR providing a greater amount of valuable information (p = 0.007), improvement in the productivity by EHR use (p < 0.001), and a statement that using EHR is a good idea (p = 0.014). Overall the percentage of correct classifications in the model was 83.9%. Conclusion: In this research, determinants of the EHR acceptance were assessed in accordance with the TAM, providing an overall good model fit. Future research should attempt to add other constructs to the TAM in order to fully identify all determinants of physician acceptance of EHR in the complex environment of different health systems.

scholarly journals Practice Facilitator Strategies for Addressing Electronic Health Record Data Challenges for Quality Improvement: EvidenceNOW

2018 ◽  
Vol 31 (3) ◽  
pp. 398-409 ◽  
Author(s):  
Jennifer R. Hemler ◽  
Jennifer D. Hall ◽  
Raja A. Cholan ◽  
Benjamin F. Crabtree ◽  
Laura J. Damschroder ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

scholarly journals Implementing a quality improvement initiative for the management of chronic obstructive pulmonary disease in rural Nepal

2019 ◽  
Vol 8 (1) ◽  
pp. e000408 ◽  
Author(s):  
Stephen Mehanni ◽  
Dhiraj Jha ◽  
Anirudh Kumar ◽  
Nandini Choudhury ◽  
Binod Dangal ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Quality Improvement ◽  
Disease Management ◽  
Electronic Health Record ◽  
Pulmonary Disease ◽  
Chronic Obstructive ◽  
Health Record ◽  
Obstructive Pulmonary Disease ◽  
Low Middle Income Countries ◽  
Electronic Health

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.

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

2019 ◽  
Vol 24 (6) ◽  
pp. 230-237 ◽  
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.

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

ACI Open ◽  
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.

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