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What Can Apologies in the Electronic Health Record Tell Us About Health Care Quality, Processes, and Safety?

ABSTRACT: INTRODUCTION:Apologizing to patients is an encouraged practice, yet little is known about how and why providers apologize and what insights apologies could provide in improving quality and safety. OBJECTIVE:The aim of the study was to determine whether provider apologies in the electronic health record could identify patient safety concerns and opportunities for improvement. METHODS:After performing a free-text search, we randomly selected 100 clinical notes from 1685 available containing terminology related to apology. We categorized the reason for apology, presence and classification of medical error, level of patient harm, and practice improvement opportunities. We compared patient events discovered from apologies in the medical record to standard patient incident report logs. RESULTS:Of 100 randomly selected apologies, 37 were related to a delay in care, 14 to misunderstanding, 11 to access to care, and 8 to information technology. For apologies related to delay, the median delay was 6 days (mean = 8.9, range = 0-41). Twenty-four (65%) of the 37 delays were related to diagnostic testing.Medical errors were associated with 46 (46%) of the 100 apologies. Sixty-four (64%) of the 100 apologies were associated with actionable opportunities for improvement. These opportunities were classified into 37 discrete issues across 8 broad categories. When apology review was compared with standard incident report logs, 27 (73%) of the 37 discrete issues identified by patient apology review were not found in incident reporting; both methods identified similar rates of patient harm. CONCLUSIONS:Review of apologies in the electronic health record can identify patient safety concerns and improvement opportunities not apparent through standard incident reporting.

SUBMITTER: Matulis JC

PROVIDER: S-EPMC7447129 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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What Can Apologies in the Electronic Health Record Tell Us About Health Care Quality, Processes, and Safety?

Matulis John C JC North Frederick F

Journal of patient safety 20200901 3

<h4>Introduction</h4>Apologizing to patients is an encouraged practice, yet little is known about how and why providers apologize and what insights apologies could provide in improving quality and safety.<h4>Objective</h4>The aim of the study was to determine whether provider apologies in the electronic health record could identify patient safety concerns and opportunities for improvement.<h4>Methods</h4>After performing a free-text search, we randomly selected 100 clinical notes from 1685 avail ...[more]

PMID: 30110020

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Project description:BACKGROUND:The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs. METHODS:Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products. RESULTS:A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight). CONCLUSIONS:This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

Project description:ImportanceDespite billions spent in public investment, electronic health records (EHRs) have not delivered on the promise of large quality and safety improvement. Simultaneously, there is debate on whether public quality reporting is a useful tool to incentivize quality improvement.ObjectiveTo evaluate whether publicly reported feedback was associated with hospital improvement in an evaluation of medication-related clinical decision support (CDS) safety performance.Design, settings, and participantsThis nonrandomized controlled trial included US hospitals that participated in the Computerized Provider Order Entry (CPOE) Evaluation Tool in the Leapfrog Hospital Survey, a national quality reporting program that evaluates safety performance of hospital CDS using simulated orders and patients, in 2017 to 2018. A sharp regression discontinuity design was used to identify the association of receiving negative feedback with hospital performance improvement in the subsequent year. Data were analyzed from January through September 2020.ExposuresPublicly reported quality feedback.Main outcomes and measuresThe main outcome was improvement from 2017 to 2018 on the Leapfrog CPOE Evaluation Tool, using regression discontinuity model estimates of the association of receiving negative publicly reported feedback with quality improvement.ResultsA total of 1183 hospitals were included, with a mean (SD) CPOE score of 59.3% (16.3%) at baseline. Hospitals receiving negative feedback improved 8.44 (95% CI, 0.09 to 16.80) percentage points more in the subsequent year compared with hospitals that received positive feedback on the same evaluation. This change was driven by differences in improvement in basic CDS capabilities (β = 8.71 [95%CI, 1.67 to 18.73]) rather than advanced CDS (β = 6.15 [95% CI, -9.11 to 26.83]).Conclusions and relevanceIn this nonrandomized controlled trial, publicly reported feedback was associated with quality improvement, suggesting targeted measurement and reporting of process quality may be an effective policy lever to encourage improvement in specific areas. Clinical decision support represents an important tool in ensuring patient safety and decreasing adverse drug events, especially for complex patients and those with multiple chronic conditions who often receive several different drugs during an episode of care.

Project description:BackgroundThe rapid expansion in the use of electronic health records (EHR) has increased the number of medical errors originating in health information systems (HIS). The sociotechnical approach helps in understanding risks in the development, implementation, and use of EHR and health information technology (HIT) while accounting for complex interactions of technology within the health care system.ObjectiveThis study addresses two important questions: (1) "which of the common EHR error types are associated with perceived high- and extreme-risk severity ratings among EHR users?", and (2) "which variables are associated with high- and extreme-risk severity ratings?"MethodsThis study was a quantitative, non-experimental, descriptive study of EHR users. We conducted a cross-sectional web-based questionnaire study at the largest hospital district in Finland. Statistical tests included the reliability of the summative scales tested with Cronbach's alpha. Logistic regression served to assess the association of the independent variables to each of the eight risk factors examined.ResultsA total of 2864 eligible respondents provided the final data. Almost half of the respondents reported a high level of risk related to the error type "extended EHR unavailability". The lowest overall risk level was associated with "selecting incorrectly from a list of items". In multivariate analyses, profession and clinical unit proved to be the strongest predictors for high perceived risk. Physicians perceived risk levels to be the highest ( P<.001 in six of eight error types), while emergency departments, operating rooms, and procedure units were associated with higher perceived risk levels ( P<.001 in four of eight error types). Previous participation in eLearning courses on EHR-use was associated with lower risk for some of the risk factors.ConclusionsBased on a large number of Finnish EHR users in hospitals, this study indicates that HIT safety hazards should be taken very seriously, particularly in operating rooms, procedure units, emergency departments, and intensive care units/critical care units. Health care organizations should use proactive and systematic assessments of EHR risks before harmful events occur. An EHR training program should be compulsory for all EHR users in order to address EHR safety concerns resulting from the failure to use HIT appropriately.

Project description:BackgroundElectronic health records (EHRs) and poor system interoperability are well-known issues in the use of health information technologies in most high-income countries worldwide. Despite the abundance of literature exploring their relationship, their practical implications on patient safety and quality of care remain unclear.ObjectiveThis study aimed to examine how EHR interoperability affects patient safety, or other dimensions of care quality, in high-income health care settings.MethodsA systematic search was conducted using 4 web-based medical journal repositories and grey literature sources. The publications included were published in English between 2010 and 2022, pertaining to EHR use, interoperability, and patient safety or care quality in high-income settings. Screening was completed by 3 researchers in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Risk of bias assessments were performed using the Risk of Bias in Non-randomized Studies of Interventions and the Cochrane Risk of Bias 2 tools. The findings were presented as a narrative synthesis and mapped based on the Institute of Medicine's framework for health care quality.ResultsA total of 12 studies met the inclusion criteria to be included in our review. The findings were categorized into 6 common outcome measure categories: patient safety events, medication safety, data accuracy and errors, care effectiveness, productivity, and cost savings. EHR interoperability positively influenced medication safety, reduced patient safety events, and reduced costs. Improvements in time saving and clinical workflow are mixed. However, true measures of effect are difficult to determine with certainty because of the heterogeneity in the outcome measures used and notable variation in study quality.ConclusionsThe benefits of EHR interoperability on the quality and safety of care remain unclear and reflect extensive heterogeneity in the interventions, designs, and outcome measures used. The establishment of common health information technology research outcome measures would support higher-quality research on the topic. Future research efforts should focus on both the positive and negative impacts of interoperable EHR interventions and explore patient perspectives, given the growing trend for patient involvement and stewardship over their own electronic clinical data.Trial registrationPROSPERO CRD42020209285; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=209285.International registered report identifier (irrid)RR2-10.1136/bmjopen-2020-044941.

Project description:BackgroundMultiple policy initiatives encourage more cautious prescribing of opioids in light of their risks. Electronic health record (EHR) redesign can influence prescriber choices, but some redesigns add to workload.ObjectiveTo estimate the effect of an EHR prescribing redesign on both opioid prescribing choices and keystrokes.DesignQuality improvement quasi-experiment, analyzed as interrupted time series.ParticipantsAdult patients of an academic multispecialty practice and a federally qualified health center (FQHC) who received new prescriptions for short-acting opioids, and their providers.InterventionIn the redesign, new prescriptions of short-acting opioids defaulted to the CDC-recommended minimum for opioid-naïve patients, with no alerts or hard stops, such that 9 keystrokes were required for a guideline-concordant prescription and 24 for a non-concordant prescription.Main measuresProportion of guideline-concordant prescriptions, defined as new prescriptions with a 3-day supply or less, calculated per 2-week period. Number of mouse clicks and keystrokes needed to place prescriptions.Key resultsAcross the 2 sites, 22,113 patients received a new short-acting opioid prescription from 821 providers. Before the intervention, both settings showed secular trends toward smaller-quantity prescriptions. At the academic practice, the intervention was associated with an immediate increase in guideline-concordant prescriptions from an average of 12% to 31% of all prescriptions. At the FQHC, about 44% of prescriptions were concordant at the time of the intervention, which was not associated with an additional significant increase. However, total keystrokes needed to place the concordant prescriptions decreased 62.7% from 3552 in the 6 months before the intervention to 1323 in the 6 months afterwards.ConclusionsAutocompleting prescription forms with guideline-recommended values was associated with a large increase in guideline concordance in an organization where baseline concordance was low, but not in an organization where it was already high. The redesign markedly reduced the number of keystrokes needed to place orders, with important implications for EHR-related stress.Trial registrationwww.ClinicalTrials.gov protocol 1710018646.

Dataset Information

What Can Apologies in the Electronic Health Record Tell Us About Health Care Quality, Processes, and Safety?

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What Can Apologies in the Electronic Health Record Tell Us About Health Care Quality, Processes, and Safety?

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