Dataset Information

Clinical documentation of dental care in an era of electronic health record use.

ABSTRACT: Although complete and accurate clinical records do not guarantee the provision of excellent dental care, they do provide an opportunity to evaluate the quality of care provided. However, a lack of universally accepted documentation standards, incomplete record-keeping practices, and unfriendly electronic health care record (EHR) user interfaces are factors that have allowed for persistent poor dental patient record keeping.Using 2 different methods-a validated survey, and a 2-round Delphi process-involving 2 appropriately different sets of participants, we explored what a dental clinical record should contain and the frequency of update of each clinical entry.For both the closed-ended survey questions and the open-ended Delphi process questions, respondents had a significant degree of agreement on the "clinical entry" components of an adequate clinical record. There was, however, variance on how frequently each of those clinical entries should be updated.Dental providers agree that complete and accurate record keeping is essential and that items such as histories, examination findings, diagnosis, radiographs, treatment plans, consents, and clinic notes should be documented. There, however, does not seem to be universal agreement how frequently such items should be recorded.As the dental profession moves towards prevalent use of electronic health care records, the issue of standardization and interoperability becomes ever more pressing. Settling issues of standardization, including record documentation, must begin with guideline-creating dental professional bodies, who need to clearly define and disseminate what these standards should be and everyday dentists who will ultimately ensure that these standards are met and kept.

SUBMITTER: Tokede O

PROVIDER: S-EPMC5119920 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clinical documentation of dental care in an era of electronic health record use.

Tokede Oluwabunmi O Ramoni Rachel B RB Patton Michael M Da Silva John D JD Kalenderian Elsbeth E

The journal of evidence-based dental practice 20160721 3

<h4>Background</h4>Although complete and accurate clinical records do not guarantee the provision of excellent dental care, they do provide an opportunity to evaluate the quality of care provided. However, a lack of universally accepted documentation standards, incomplete record-keeping practices, and unfriendly electronic health care record (EHR) user interfaces are factors that have allowed for persistent poor dental patient record keeping.<h4>Methods</h4>Using 2 different methods-a validated ...[more]

PMID: 27855830

Similar Datasets

Project description:ImportanceElectronic health records are a potentially valuable source of information for identifying patients with opioid use disorder (OUD).ObjectiveTo evaluate whether proxy measures from electronic health record data can be used reliably to identify patients with probable OUD based on Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria.Design, setting, and participantsThis retrospective cross-sectional study analyzed individuals within the Geisinger health system who were prescribed opioids between December 31, 2000, and May 31, 2017, using a mixed-methods approach. The cohort was identified from 16 253 patients enrolled in a contract-based, Geisinger-specific medication monitoring program (GMMP) for opioid use, including patients who maintained or violated contract terms, as well as a demographically matched control group of 16 253 patients who were prescribed opioids but not enrolled in the GMMP. Substance use diagnoses and psychiatric comorbidities were assessed using automated electronic health record summaries. A manual medical record review procedure using DSM-5 criteria for OUD was completed for a subset of patients. The analysis was conducted beginning from June 5, 2017, until May 29, 2020.Main outcomes and measuresThe primary outcome was the prevalence of OUD as defined by proxy measures for DSM-5 criteria for OUD as well as the prevalence of comorbidities among patients prescribed opioids within an integrated health system.ResultsAmong the 16 253 patients enrolled in the GMMP (9309 women [57%]; mean [SD] age, 52 [14] years), OUD diagnoses as defined by diagnostic codes were present at a much lower rate than expected (291 [2%]), indicating the necessity for alternative diagnostic strategies. The DSM-5 criteria for OUD can be assessed using manual medical record review; a manual review of 200 patients in the GMMP and 200 control patients identifed a larger percentage of patients with probable moderate to severe OUD (GMMP, 145 of 200 [73%]; and control, 27 of 200 [14%]) compared with the prevalence of OUD assessed using diagnostic codes.Conclusions and relevanceThese results suggest that patients with OUD may be identified using information available in the electronic health record, even when diagnostic codes do not reflect this diagnosis. Furthermore, the study demonstrates the utility of coding for DSM-5 criteria from medical records to generate a quantitative DSM-5 score that is associated with OUD severity.

Project description:ImportanceAccurate measurements of clinical workload are needed to inform health care policy. Existing methods for measuring clinical workload rely on surveys or time-motion studies, which are labor-intensive to collect and subject to biases.ObjectiveTo compare anesthesia clinical workload estimated from electronic health record (EHR) audit log data vs billed relative value units.Design, setting, and participantsThis cross-sectional study of anesthetic encounters occurring between August 26, 2019, and February 9, 2020, used data from 8 academic hospitals, community hospitals, and surgical centers across Missouri and Illinois. Clinicians who provided anesthetic services for at least 1 surgical encounter were included. Data were analyzed from January 2022 to January 2023.ExposureAnesthetic encounters associated with a surgical procedure were included. Encounters associated with labor analgesia and endoscopy were excluded.Main outcomes and measuresFor each encounter, EHR-derived clinical workload was estimated as the sum of all EHR actions recorded in the audit log by anesthesia clinicians who provided care. Billing-derived clinical workload was measured as the total number of units billed for the encounter. A linear mixed-effects model was used to estimate the relative contribution of patient complexity (American Society of Anesthesiology [ASA] physical status modifier), procedure complexity (ASA base unit value for the procedure), and anesthetic duration (time units) to EHR-derived and billing-derived workload. The resulting β coefficients were interpreted as the expected effect of a 1-unit change in each independent variable on the standardized workload outcome. The analysis plan was developed after the data were obtained.ResultsA total of 405 clinicians who provided anesthesia for 31 688 encounters were included in the study. A total of 8 288 132 audit log actions corresponding to 39 131 hours of EHR use were used to measure EHR-derived workload. The contributions of patient complexity, procedural complexity, and anesthesia duration to EHR-derived workload differed significantly from their contributions to billing-derived workload. The contribution of patient complexity toward EHR-derived workload (β = 0.162; 95% CI, 0.153-0.171) was more than 50% greater than its contribution toward billing-derived workload (β = 0.106; 95% CI, 0.097-0.116; P < .001). In contrast, the contribution of procedure complexity toward EHR-derived workload (β = 0.033; 95% CI, 0.031-0.035) was approximately one-third its contribution toward billing-derived workload (β = 0.106; 95% CI, 0.104-0.108; P < .001).Conclusions and relevanceIn this cross-sectional study of 8 hospitals, reimbursement for anesthesiology services overcompensated for procedural complexity and undercompensated for patient complexity. This method for measuring clinical workload could be used to improve reimbursement valuations for anesthesia and other specialties.

Project description:Although standardized terminologies such as the International Classification of Diseases have been in use in medicine for over a century, efforts in the dental profession to standardize dental diagnostic terms have not achieved widespread acceptance. To address this gap, a standardized dental diagnostic terminology, the EZCodes, was developed in 2009. Fifteen dental education institutions in the United States and Europe have implemented the EZCodes dental diagnostic terminology. This article reports on the utilization and valid entry of the EZCodes at three of the dental schools that have adopted this standardized dental diagnostic terminology. Electronic data on the use of procedure codes with diagnostic terms from the three schools over a period from July 2010 to June 2011 were aggregated. The diagnostic term and procedure code pairs were adjudicated by three calibrated dentists. Analyses were conducted to gain insight into the utilization and valid entry of the EZCodes diagnostic terminology in the one-year period. Error proportions in the entry of diagnostic term (and by diagnostic category) were also computed. In the twelve-month period, 29,965 diagnostic terms and 249,411 procedure codes were entered at the three institutions resulting in a utilization proportion of 12 percent. Caries and periodontics were the most frequently used categories. More than 1,000 of the available 1,321 diagnostic terms were never used. Overall, 60.5 percent of the EZCodes entries were found to be valid. The results demonstrate low utilization of EZCodes in an electronic health record and raise the need for specific training of dental providers on the importance of using dental diagnostic terminology and specifically how to use the terms in the electronic record. These findings will serve to increase the use/correct use of the EZCodes dental diagnostic terminology and ultimately create a reliable platform for undertaking clinical, outcomes, and quality improvement-related research.

Project description:BackgroundElectronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research.ObjectiveThe objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences.MethodsWe used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results.ResultsWe present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized.ConclusionsThe Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.

Dataset Information

Clinical documentation of dental care in an era of electronic health record use.

Publications

Clinical documentation of dental care in an era of electronic health record use.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets