Formal representation of patients' care context data: the path to improving the electronic health record.
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ABSTRACT:
SUBMITTER: Colicchio TK
PROVIDER: S-EPMC7671623 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
Publications
Formal representation of patients' care context data: the path to improving the electronic health record.
Colicchio Tiago K TK Dissanayake Pavithra I PI Cimino James J JJ
Journal of the American Medical Informatics Association : JAMIA 20201101 11
<h4>Objective</h4>To develop a collection of concept-relationship-concept tuples to formally represent patients' care context data to inform electronic health record (EHR) development.<h4>Materials and methods</h4>We reviewed semantic relationships reported in the literature and developed a manual annotation schema. We used the initial schema to annotate sentences extracted from narrative note sections of cardiology, urology, and ear, nose, and throat (ENT) notes. We audio recorded ENT visits an ...[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.
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Project description:BackgroundCare plan concordance among patients and clinicians during hospitalization is suboptimal.ObjectiveThis article determines whether an electronic health record (EHR)-integrated patient portal was associated with increased understanding of the care plan, including the key recovery goal, among patients and clinicians in acute care setting.MethodsThe intervention included (1) a patient portal configured to solicit a single patient-designated recovery goal and display the care plan from the EHR for participating patients; and (2) an electronic care plan for all unit-based nurses that displays patient-inputted information, accessible to all clinicians via the EHR. Patients admitted to an oncology unit, including their nurses and physicians, were enrolled before and after implementation. Main outcomes included mean concordance scores for the overall care plan and individual care plan elements.ResultsOf 457 and 283 eligible patients approached during pre- and postintervention periods, 55 and 46 participated in interviews, respectively, including their clinicians. Of 46 postintervention patients, 27 (58.7%) enrolled in the patient portal. The intention-to-treat analysis demonstrated a nonsignificant increase in the mean concordance score for the overall care plan (62.0-67.1, adjusted p = 0.13), and significant increases in mean concordance scores for the recovery goal (30.3-57.7, adjusted p < 0.01) and main reason for hospitalization (58.6-79.2, adjusted p < 0.01). The on-treatment analysis of patient portal enrollees demonstrated significant increases in mean concordance scores for the overall care plan (61.9-70.0, adjusted p < 0.01), the recovery goal (30.4-66.8, adjusted p < 0.01), and main reason for hospitalization (58.3-81.7, adjusted p < 0.01), comparable to the intention-to-treat analysis.ConclusionImplementation of an EHR-integrated patient portal was associated with increased concordance for key care plan components. Future efforts should be directed at improving concordance for other care plan components and conducting larger, randomized studies to evaluate the impact on key outcomes during transitions of care.Clinical trials identifierNCT02258594.
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Project description:PurposeIntegrating genomic data into the electronic health record (EHR) is key for optimally delivering genomic medicine.MethodsThe PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative that has successfully linked orders and results from genetic testing laboratories with discrete genetic data in the EHR. We quantified the use of the genomic data within the EHR, performed a time study with genetic counselors, and conducted key informant interviews with PGI members to evaluate the effect of the PGI's efforts on genetics care delivery.ResultsThe PGI has interfaced with 4 genetic testing laboratories, resulting in the creation of 420 unique computerized genetic testing orders that have been used 4073 times to date. In a time study of 96 genetic testing activities, EHR use was associated with significant reductions in time spent ordering (2 vs 8 minutes, P < .001) and managing (1 vs 5 minutes, P < .001) genetic results compared with the use of online laboratory-specific portals. In key informant interviews, multidisciplinary collaboration and institutional buy-in were identified as key ingredients for the PGI's success.ConclusionThe PGI's efforts to integrate genomic medicine into the EHR have substantially streamlined the delivery of genomic medicine.
| S-EPMC10176082 | 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
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.
| S-EPMC7878599 | biostudies-literature