Dataset Information

Feasibility of real-time capture of routine clinical data in the electronic health record: a hospital-based, observational service-evaluation study.

ABSTRACT: OBJECTIVES:The electronic health record (EHR) is underused in the hospital setting. The aim of this service evaluation study was to respond to National Health Service (NHS) Digital's ambition for a paperless NHS by capturing routinely collected cardiac outpatient data in the EHR to populate summary patient reports and provide a resource for audit and research. DESIGN:A PowerForm template was developed within the Cerner EHR, for real-time entry of routine clinical data by clinicians attending a cardiac outpatient clinic. Data captured within the PowerForm automatically populated a SmartTemplate to generate a view-only report that was immediately available for the patient and for electronic transmission to the referring general practitioner (GP). RESULTS:During the first 8?months, the PowerForm template was used in 61% (360/594) of consecutive outpatient referrals increasing from 42% to 77% during the course of the study. Structured patient reports were available for immediate sharing with the referring GP using Cerner Health Information Exchange technology while electronic transmission was successfully developed in a substudy of 64 cases, with direct delivery by the NHS Data Transfer Service in 29 cases and NHS mail in the remainder. In feedback, the report's immediate availability was considered very or extremely important by >80%?of the patients and GPs who were surveyed. Both groups reported preference of the patient report to the conventional typed letter. Deidentified template data for all 360 patients were successfully captured within the Trust system, confirming availability of these routinely collected outpatient data for audit and research. CONCLUSION:Electronic template development tailored to the requirements of a specialist outpatient clinic facilitates capture of routinely collected data within the Cerner EHR. These data can be made available for audit and research. They can also be used to enhance communication by populating structured reports for immediate delivery to patients and GPs.

SUBMITTER: Bodagh N

PROVIDER: S-EPMC5855191 | biostudies-other | 2018 Mar

REPOSITORIES: biostudies-other

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Feasibility of real-time capture of routine clinical data in the electronic health record: a hospital-based, observational service-evaluation study.

Bodagh Neil N Archbold R Andrew RA Weerackody Roshan R Hawking Meredith K D MKD Barnes Michael R MR Lee Aaron M AM Janjuha Surjeet S Gutteridge Charles C Robson John J Timmis Adam A

BMJ open 20180308 3

<h4>Objectives</h4>The electronic health record (EHR) is underused in the hospital setting. The aim of this service evaluation study was to respond to National Health Service (NHS) Digital's ambition for a paperless NHS by capturing routinely collected cardiac outpatient data in the EHR to populate summary patient reports and provide a resource for audit and research.<h4>Design</h4>A PowerForm template was developed within the Cerner EHR, for real-time entry of routine clinical data by clinician ...[more]

PMID: 29523565

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Project description:BACKGROUND:The secondary use of electronic health records (EHRs) promises to facilitate medical research. We reviewed general data requirements in observational studies and analyzed the feasibility of conducting observational studies with structured EHR data, in particular diagnosis and procedure codes. METHODS:After reviewing published observational studies from the University Hospital of Erlangen for general data requirements, we identified three different study populations for the feasibility analysis with eligibility criteria from three exemplary observational studies. For each study population, we evaluated the availability of relevant patient characteristics in our EHR, including outcome and exposure variables. To assess data quality, we computed distributions of relevant patient characteristics from the available structured EHR data and compared them to those of the original studies. We implemented computed phenotypes for patient characteristics where necessary. In random samples, we evaluated how well structured patient characteristics agreed with a gold standard from manually interpreted free texts. We categorized our findings using the four data quality dimensions "completeness", "correctness", "currency" and "granularity". RESULTS:Reviewing general data requirements, we found that some investigators supplement routine data with questionnaires, interviews and follow-up examinations. We included 847 subjects in the feasibility analysis (Study 1 n?=?411, Study 2 n?=?423, Study 3 n?=?13). All eligibility criteria from two studies were available in structured data, while one study required computed phenotypes in eligibility criteria. In one study, we found that all necessary patient characteristics were documented at least once in either structured or unstructured data. In another study, all exposure and outcome variables were available in structured data, while in the other one unstructured data had to be consulted. The comparison of patient characteristics distributions, as computed from structured data, with those from the original study yielded similar distributions as well as indications of underreporting. We observed violations in all four data quality dimensions. CONCLUSIONS:While we found relevant patient characteristics available in structured EHR data, data quality problems may entail that it remains a case-by-case decision whether diagnosis and procedure codes are sufficient to underpin observational studies. Free-text data or subsequently supplementary study data may be important to complement a comprehensive patient history.

Project description:BACKGROUND:The rapid implementation of patient portals, through which patients can view their electronic health record, creates possibilities for information exchange and communication between patients and health care professionals. However, real-time disclosure of test results and clinical reports poses a source of concern. OBJECTIVE:This study aimed to examine negative experiences resulting from real-time disclosure of medical information through a patient portal. METHODS:Data were collected over a 2-year period in 4 datasets consisting of incidents reported by health care professionals, complaints of patients, patient issues at a portal helpdesk, and a survey among health care professionals. Incidents, complaints, issues, and answers on the survey were counted and analyzed through an iterative process of coding. RESULTS:Within the chosen time frame of 2 years, on average, 7978 patients per month logged into the portal at least once. The amount of negative incidents and complaints was limited. A total of 6 incidents, 4 complaints, and 2506 issues at the helpdesk concerning the patient portal were reported, of which only 2, 1, and 3 cases of these respective databases concerned real-time disclosure of medical information through the patient portal. Moreover, 32 out of 216 health care professionals reported patients that had negative experiences with real-time disclosure. Most negative consequences concerned confused and anxious patients when confronted with unexpected or incomprehensible results. CONCLUSIONS:Real-time access through a patient portal did not substantially result in negative consequences. The negative consequences that did occur can be mitigated by adequate preparation and instruction of patients concerning the various functionalities of the patient portal, real-time disclosure of test results in particular, and can also be managed through educating health care professionals about the patient portal and making adjustments in the daily practice of health care professionals.

Project description:ObjectivesAnalysis of routinely collected electronic health data is a key tool for long-term condition research and practice for hospitalised patients. This requires accurate and complete ascertainment of a broad range of diagnoses, something not always recorded on an admission document at a single point in time. This study aimed to ascertain how far back in time electronic hospital records need to be interrogated to capture long-term condition diagnoses.DesignRetrospective observational study of routinely collected hospital electronic health record data.SettingQueen Elizabeth Hospital Birmingham (UK)-linked data held by the PIONEER acute care data hub.ParticipantsPatients whose first recorded admission for chronic obstructive pulmonary disease (COPD) exacerbation (n=560) or acute stroke (n=2142) was between January and December 2018 and who had a minimum of 10 years of data prior to the index date.Outcome measuresWe identified the most common International Classification of Diseases version 10-coded diagnoses received by patients with COPD and acute stroke separately. For each diagnosis, we derived the number of patients with the diagnosis recorded at least once over the full 10-year lookback period, and then compared this with shorter lookback periods from 1 year to 9 years prior to the index admission.ResultsSeven of the top 10 most common diagnoses in the COPD dataset reached >90% completeness by 6 years of lookback. Atrial fibrillation and diabetes were >90% coded with 2-3 years of lookback, but hypertension and asthma completeness continued to rise all the way out to 10 years of lookback. For stroke, 4 of the top 10 reached 90% completeness by 5 years of lookback; angina pectoris was >90% coded at 7 years and previous transient ischaemic attack completeness continued to rise out to 10 years of lookback.ConclusionA 7-year lookback captures most, but not all, common diagnoses. Lookback duration should be tailored to the conditions being studied.

Project description:ImportanceNumerous prognostic models of suicide risk have been published, but few have been implemented outside of integrated managed care systems.ObjectiveTo evaluate performance of a suicide attempt risk prediction model implemented in a vendor-supplied electronic health record to predict subsequent (1) suicidal ideation and (2) suicide attempt.Design, setting, and participantsThis observational cohort study evaluated implementation of a suicide attempt prediction model in live clinical systems without alerting. The cohort comprised patients seen for any reason in adult inpatient, emergency department, and ambulatory surgery settings at an academic medical center in the mid-South from June 2019 to April 2020.Main outcomes and measuresPrimary measures assessed external, prospective, and concurrent validity. Manual medical record validation of coded suicide attempts confirmed incident behaviors with intent to die. Subgroup analyses were performed based on demographic characteristics, relevant clinical context/setting, and presence or absence of universal screening. Performance was evaluated using discrimination (number needed to screen, C statistics, positive/negative predictive values) and calibration (Spiegelhalter z statistic). Recalibration was performed with logistic calibration.ResultsThe system generated 115 905 predictions for 77 973 patients (42 490 [54%] men, 35 404 [45%] women, 60 586 [78%] White, 12 620 [16%] Black). Numbers needed to screen in highest risk quantiles were 23 and 271 for suicidal ideation and attempt, respectively. Performance was maintained across demographic subgroups. Numbers needed to screen for suicide attempt by sex were 256 for men and 323 for women; and by race: 373, 176, and 407 for White, Black, and non-White/non-Black patients, respectively. Model C statistics were, across the health system: 0.836 (95% CI, 0.836-0.837); adult hospital: 0.77 (95% CI, 0.77-0.772); emergency department: 0.778 (95% CI, 0.777-0.778); psychiatry inpatient settings: 0.634 (95% CI, 0.633-0.636). Predictions were initially miscalibrated (Spiegelhalter z = -3.1; P = .001) with improvement after recalibration (Spiegelhalter z = 1.1; P = .26).Conclusions and relevanceIn this study, this real-time predictive model of suicide attempt risk showed reasonable numbers needed to screen in nonpsychiatric specialty settings in a large clinical system. Assuming that research-valid models will translate without performing this type of analysis risks inaccuracy in clinical practice, misclassification of risk, wasted effort, and missed opportunity to correct and prevent such problems. The next step is careful pairing with low-cost, low-harm preventive strategies in a pragmatic trial of effectiveness in preventing future suicidality.

Dataset Information

Feasibility of real-time capture of routine clinical data in the electronic health record: a hospital-based, observational service-evaluation study.

Publications

Feasibility of real-time capture of routine clinical data in the electronic health record: a hospital-based, observational service-evaluation study.

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