Systematic review of scope and quality of electronic patient record data in primary care.
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ABSTRACT:
SUBMITTER: Thiru K
PROVIDER: S-EPMC155692 | biostudies-literature | 2003 May
REPOSITORIES: biostudies-literature
Publications
Systematic review of scope and quality of electronic patient record data in primary care.
Thiru Krish K Hassey Alan A Sullivan Frank F
BMJ (Clinical research ed.) 20030501 7398
<h4>Objective</h4>To systematically review measures of data quality in electronic patient records (EPRs) in primary care.<h4>Design</h4>Systematic review of English language publications, 1980-2001.<h4>Data sources</h4>Bibliographic searches of medical databases, specialist medical informatics databases, conference proceedings, and institutional contacts.<h4>Study selection</h4>Studies selected according to a predefined framework for categorising review papers.<h4>Data extraction</h4>Reference s ...[more]
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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.
| S-EPMC6373085 | biostudies-other
Project description:ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.DesignA systematic review of published studies.Data sourcesPubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.Study selectionObservational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.Results6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.ConclusionsComparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.
| S-EPMC7559041 | biostudies-literature
Project description:ObjectiveQuality indicators for the treatment of type 2 diabetes are often retrieved from a chronic disease registry (CDR). This study investigates the quality of recording in a general practitioner's (GP) electronic medical record (EMR) compared to a simple, web-based CDR.MethodsThe GPs entered data directly in the CDR and in their own EMR during the study period (2011). We extracted data from 58 general practices (8235 patients) with type 2 diabetes and compared the occurrence and value of seven process indicators and 12 outcome indicators in both systems. The CDR, specifically designed for monitoring type 2 diabetes and reporting to health insurers, was used as the reference standard. For process indicators we examined the presence or absence of recordings on the patient level in both systems, for outcome indicators we examined the number of compliant or non-compliant values of recordings present in both systems. The diagnostic OR (DOR) was calculated for all indicators.ResultsWe found less concordance for process indicators than for outcome indicators. HbA1c testing was the process indicator with the highest DOR. Blood pressure measurement, urine albumin test, BMI recorded and eye assessment showed low DOR. For outcome indicators, the highest DOR was creatinine clearance <30 mL/min or mL/min/1.73 m(2) and the lowest DOR was systolic blood pressure <140 mm Hg.ConclusionsClinical items are not always adequately recorded in an EMR for retrieving indicators, but there is good concordance for the values of these items. If the quality of recording improves, indicators can be reported from the EMR, which will reduce the workload of GPs and enable GPs to maintain a good patient overview.
| S-EPMC4078269 | biostudies-literature
Project description:ObjectiveThe aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH).Materials and methodsWe conducted a systematic review of the literature on social determinants research and data quality and then iteratively identified themes in the literature using a content analysis process.ResultsThe most commonly represented quality issue associated with SDoH data is plausibility (n = 31, 41%). Factors related to race and ethnicity have the largest body of literature (n = 40, 53%). The first theme, noted in 62% (n = 47) of articles, is that bias or validity issues often result from data quality problems. The most frequently identified validity issue is misclassification bias (n = 23, 30%). The second theme is that many of the articles suggest methods for mitigating the issues resulting from poor social determinants data quality. We grouped these into 5 suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully.DiscussionThe type of data quality problem varies depending on the variable, and each problem is associated with particular forms of analytical error. Problems encountered with the quality of SDoH data are rarely distributed randomly. Data from Hispanic patients are more prone to issues with plausibility and misclassification than data from other racial/ethnic groups.ConclusionConsideration of data quality and evidence-based quality improvement methods may help prevent bias and improve the validity of research conducted with SDoH data.
| S-EPMC8714289 | biostudies-literature
Project description:Physicians who more intensively interact with electronic health records (EHRs) through their documentation style may pay greater attention to coded fields and clinical decision support and thus may deliver higher quality care. We measured the quality of care of physicians who used three predominating EHR documentation styles: dictation, structured documentation, and free text.We conducted a retrospective analysis of visits by patients with coronary artery disease and diabetes to the Partners Primary Care Practice Based Research Network. The main outcome measures were 15 EHR-based coronary artery disease and diabetes measures assessed 30 days after primary care visits.During the 9-month study period, 7000 coronary artery disease and diabetes patients made 18?569 visits to 234 primary care physicians of whom 20 (9%) predominantly dictated their notes, 68 (29%) predominantly used structured documentation, and 146 (62%) predominantly typed free text notes. In multivariable modeling adjusted for clustering by patient and physician, quality of care appeared significantly worse for dictators than for physicians using the other two documentation styles on three of 15 measures (antiplatelet medication, tobacco use documentation, and diabetic eye exam); better for structured documenters for three measures (blood pressure documentation, body mass index documentation, and diabetic foot exam); and better for free text documenters on one measure (influenza vaccination). There was no measure for which dictators had higher quality of care than physicians using the other two documentation styles.EHR-assessed quality is necessarily documentation-dependent, but physicians who dictated their notes appeared to have worse quality of care than physicians who used structured EHR documentation.ClinicalTrials.gov Identifier: NCT00235040.
| S-EPMC3534457 | biostudies-literature
Project description:To characterize patterns of electronic medical record (EMR) use at pediatric primary care acute visits.Direct observational study of 529 acute visits with 27 experienced pediatric clinician users.For each 20 s interval and at each stage of the visit according to the Davis Observation Code, we recorded whether the physician was communicating with the family only, using the computer while communicating, or using the computer without communication. Regression models assessed the impact of clinician, patient and visit characteristics on overall visit length, time spent interacting with families, and time spent using the computer while interacting.The mean overall visit length was 11:30 (min:sec) with 9:06 spent in the exam room. Clinicians used the EMR during 27% of exam room time and at all stages of the visit (interacting, chatting, and building rapport; history taking; formulation of the diagnosis and treatment plan; and discussing prevention) except the physical exam. Communication with the family accompanied 70% of EMR use. In regression models, computer documentation outside the exam room was associated with visits that were 11% longer (p=0.001), and female clinicians spent more time using the computer while communicating (p=0.003).The 12 study practices shared one EMR.Among pediatric clinicians with EMR experience, conversation accompanies most EMR use. Our results suggest that efforts to improve EMR usability and clinician EMR training should focus on use in the context of doctor-patient communication. Further study of the impact of documentation inside versus outside the exam room on productivity is warranted.
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Project description:Backgroundfrailty is an especially problematic expression of population ageing. International guidelines recommend routine identification of frailty to provide evidence-based treatment, but currently available tools require additional resource.Objectivesto develop and validate an electronic frailty index (eFI) using routinely available primary care electronic health record data.Study design and settingretrospective cohort study. Development and internal validation cohorts were established using a randomly split sample of the ResearchOne primary care database. External validation cohort established using THIN database.Participantspatients aged 65-95, registered with a ResearchOne or THIN practice on 14 October 2008.Predictorswe constructed the eFI using the cumulative deficit frailty model as our theoretical framework. The eFI score is calculated by the presence or absence of individual deficits as a proportion of the total possible. Categories of fit, mild, moderate and severe frailty were defined using population quartiles.Outcomesoutcomes were 1-, 3- and 5-year mortality, hospitalisation and nursing home admission.Statistical analysishazard ratios (HRs) were estimated using bivariate and multivariate Cox regression analyses. Discrimination was assessed using receiver operating characteristic (ROC) curves. Calibration was assessed using pseudo-R(2) estimates.Resultswe include data from a total of 931,541 patients. The eFI incorporates 36 deficits constructed using 2,171 CTV3 codes. One-year adjusted HR for mortality was 1.92 (95% CI 1.81-2.04) for mild frailty, 3.10 (95% CI 2.91-3.31) for moderate frailty and 4.52 (95% CI 4.16-4.91) for severe frailty. Corresponding estimates for hospitalisation were 1.93 (95% CI 1.86-2.01), 3.04 (95% CI 2.90-3.19) and 4.73 (95% CI 4.43-5.06) and for nursing home admission were 1.89 (95% CI 1.63-2.15), 3.19 (95% CI 2.73-3.73) and 4.76 (95% CI 3.92-5.77), with good to moderate discrimination but low calibration estimates.Conclusionsthe eFI uses routine data to identify older people with mild, moderate and severe frailty, with robust predictive validity for outcomes of mortality, hospitalisation and nursing home admission. Routine implementation of the eFI could enable delivery of evidence-based interventions to improve outcomes for this vulnerable group.
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Project description:ObjectivesTo determine whether paperless medical records contained less information than paper based medical records and whether that information was harder to retrieve.DesignCross sectional study with review of medical records and interviews with general practitioners.Setting25 general practices in Trent region.Participants53 British general practitioners (25 using paperless records and 28 using paper based records) who each provided records of 10 consultations.Main outcome measuresContent of a sample of records and doctor recall of consultations for which paperless or paper based records had been made.ResultsCompared with paper based records, more paperless records were fully understandable (89.2% v 69.9%, P=0.0001) and fully legible (100% v 64.3%, P < 0.0001). Paperless records were significantly more likely to have at least one diagnosis recorded (48.2% v 33.2%, P=0.05), to record that advice had been given (23.7% vs 10.7%, P=0.017), and, when a referral had been made, were more likely to contain details of the specialty (77.4% v 59.5%, P=0.03). When a prescription had been issued, paperless records were more likely to specify the drug dose (86.6% v 66.2%, P=0.005). Paperless records contained significantly more words, abbreviations, and symbols (P < 0.01 for all). At doctor interview, there was no difference between the groups for the proportion of patients or consultations that could be recalled. Doctors using paperless records were able to recall more advice given to patients (38.6% v 26.8%, P=0.03).ConclusionWe found no evidence to support our hypotheses that paperless records would be truncated and contain more local abbreviations; and that the absence of writing would decrease subsequent recall. Conversely we found that the paperless records compared favourably with manual records.
| S-EPMC162256 | biostudies-literature
Project description:BackgroundOlder adults are increasingly accessing information and communicating using patient-facing portals available through their providers' electronic health record (EHR). Most theories of technology acceptance and use suggest that patients' overall satisfaction with care should be independent of their chosen level of portal engagement. However, achieving expected benefits of portal use depends on demonstrated support from providers to meet these expectations. This is especially true among older adults, who may require more guidance. However, little is known about whether misalignment of expectations around technology-facilitated care is associated with lower perceptions of care quality.ObjectiveThe aims of this study were to analyze whether older adults' assessment of primary care quality differs across levels of patient portal engagement and whether perceptions of how well their provider uses the EHR to support care moderates this relationship.MethodsWe conducted a cross-sectional survey analysis of 158 older adults over the age of 65 (average age 71.4 years) across Michigan using a 13-measure composite of self-assessed health care quality. Portal use was categorized as none, moderate (use of 1-3 functionalities), or extensive (use of 4-7 functionalities). EHR value perception was measured by asking respondents how they felt their doctor's EHR use improved the patient-provider relationship.ResultsModerate portal users, compared to those who were extensive users, had lower estimated care quality (-0.214 on 4-point scale; P=.03). Differences between extensive portal users and nonportal users were not significant. Quality perception was only particularly low among moderate portal users with low EHR value perception; those with high EHR value perception rated quality similarly to other portal user groups.ConclusionsOlder adults who are moderate portal users are the least satisfied with their care, and the most sensitive to perceptions of how well their provider uses the EHR to support the relationship. Encouraging portal use without compromising perceptions of quality requires thinking beyond patient-focused education. Achieving value from use of patient-facing technologies with older adults is contingent upon matched organizational investments that support technology-enabled care delivery. Providers and staff need policies and practices that demonstrate technology adeptness. Older adults may need more tailored signaling and accommodation for technology to be maximally impactful.
| S-EPMC8145092 | biostudies-literature
Project description:BackgroundWith increased specialization of health care services and high levels of patient mobility, accessing health care services across multiple hospitals or clinics has become very common for diagnosis and treatment, particularly for patients with chronic diseases such as cancer. With informed knowledge of a patient's history, physicians can make prompt clinical decisions for smarter, safer, and more efficient care. However, due to the privacy and high sensitivity of electronic health records (EHR), most EHR data sharing still happens through fax or mail due to the lack of systematic infrastructure support for secure, trustable health data sharing, which can also cause major delays in patient care.ObjectiveOur goal was to develop a system that will facilitate secure, trustable management, sharing, and aggregation of EHR data. Our patient-centric system allows patients to manage their own health records across multiple hospitals. The system will ensure patient privacy protection and guarantee security with respect to the requirements for health care data management, including the access control policy specified by the patient.MethodsWe propose a permissioned blockchain-based system for EHR data sharing and integration. Each hospital will provide a blockchain node integrated with its own EHR system to form the blockchain network. A web-based interface will be used for patients and doctors to initiate EHR sharing transactions. We take a hybrid data management approach, where only management metadata will be stored on the chain. Actual EHR data, on the other hand, will be encrypted and stored off-chain in Health Insurance Portability and Accountability Act-compliant cloud-based storage. The system uses public key infrastructure-based asymmetric encryption and digital signatures to secure shared EHR data.ResultsIn collaboration with Stony Brook University Hospital, we developed ACTION-EHR, a system for patient-centric, blockchain-based EHR data sharing and management for patient care, in particular radiation treatment for cancer. The prototype was built on Hyperledger Fabric, an open-source, permissioned blockchain framework. Data sharing transactions were implemented using chaincode and exposed as representational state transfer application programming interfaces used for the web portal for patients and users. The HL7 Fast Healthcare Interoperability Resources standard was adopted to represent shared EHR data, making it easy to interface with hospital EHR systems and integrate a patient's EHR data. We tested the system in a distributed environment at Stony Brook University using deidentified patient data.ConclusionsWe studied and developed the critical technology components to enable patient-centric, blockchain-based EHR sharing to support cancer care. The prototype demonstrated the feasibility of our approach as well as some of the major challenges. The next step will be a pilot study with health care providers in both the United States and Switzerland. Our work provides an exemplar testbed to build next-generation EHR sharing infrastructures.
| S-EPMC7474412 | biostudies-literature