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Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters.

ABSTRACT: OBJECTIVES:The secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine. METHODS:We tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction "proton-pump inhibitor [PPI] use and osteoporosis". Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard. RESULTS:Precision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%. CONCLUSION:We could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period.

SUBMITTER: Konig M

PROVIDER: S-EPMC6881027 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters.

König Maximilian M Sander André A Demuth Ilja I Diekmann Daniel D Steinhagen-Thiessen Elisabeth E

PloS one 20191127 11

<h4>Objectives</h4>The secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a t ...[more]

PMID: 31774830

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Project description:ObjectiveTo understand how different outcomes are achieved from adult patients receiving hospital discharge letters from inpatient and outpatient settings.DesignRealist review conducted in six main steps: (1) development of initial theory, (2) searching, (3) screening and selection, (4) data extraction and analysis, (5) data synthesis and (6) programme theory (PT) refinement.Eligibility criteriaDocuments reporting evidence that met criteria for relevance to the PT. Documents relating solely to mental health or children aged <18?years were excluded.AnalysisData were extracted and analysed using a realist logic of analysis. Texts were coded for concepts relating to context, mechanism, outcome configurations (CMOCs) for the intervention of patients receiving discharge letters. All outcomes were considered. Based on evidence and our judgement, CMOCs were labelled 'positive' or 'negative' in order to clearly distinguish between contexts where the intervention does and does not work.Results3113 documents were screened and 103 were included. Stakeholders contributed to refining the PT in step 6. The final PT included 48 CMOCs for how outcomes are affected by patients receiving discharge letters. 'Patient choice' emerged as a key influencer to the success (or not) of the intervention. Important contexts were identified for both 'positive' CMOCs (eg, no new information in letter) and 'negative' CMOCs (eg, letter sent without verifying patient contact details). Two key findings were that patient understanding is possibly greater than clinicians perceive, and that patients tend to express strong preference for receiving letters. Clinician concerns emerged as a barrier to wider sharing of discharge letters with patients, which may need to be addressed through organisational policies and direction.ConclusionsThis review forms a starting point for explaining outcomes associated with whether or not patients receive discharge letters. It suggests several ways in which current processes might be modified to support improved practice and patient experience.

Project description:IntroductionDischarge documents are important for transferring information from hospitals to the referring clinician; in the UK and many countries, this is often the patient's general practitioner or family physician. However, patients may or may not receive their discharge letters, and whether patients should routinely receive discharge letters remains unclear.Methods and analysisThe review will consolidate evidence on patients receiving discharge letters through the theory-driven approach of a realist review.The review will be conducted systematically and seek to explain how, why, for whom and in what contexts does this practice 'work'. The review will specifically explore whether there are benefits of this practice and if so what are the important contexts for triggering the mechanisms associated with these outcome benefits. Negative effects will also be considered.Several steps will occur: devising initial rough programme theory, searching the evidence, selecting relevant documents, extracting data, synthesising and finally programme theory refinement. As the process is viewed as iterative, this cycle of steps may be repeated as many times as is necessary to reach theoretical saturation and may not be linear.The initial programme theory will be tested and refined throughout the review process and by stakeholder involvement of National Health Service (NHS) policy makers, practitioners and service users.Ethics and disseminationFormal ethical review is not required. The resulting programme theory is anticipated to explain how the intervention of patients receiving written discharge communication may work in practice, for whom and in what contexts; this will inform best practice of patients receiving discharge communication. The review findings will be disseminated in a peer-reviewed journal and presentations and discussions with relevant organisations and stakeholders. While the review will be from the perspective of the UK NHS, its findings should be relevant to other healthcare systems.Prospero registration numberCRD42017069863.

Project description:ImportancePostdischarge outreach from the primary care practice is an important component of transitional care support. The most common method of contact is via telephone call, but calls are labor intensive and therefore limited in scope.ObjectiveTo test whether a 30-day automated texting program to support primary care patients after hospital discharge reduces acute care revisits.Design, setting, and participantsA 2-arm randomized clinical trial was conducted from March 29, 2022, through January 5, 2023, at 30 primary care practices within a single academic health system in Philadelphia, Pennsylvania. Patients were followed up for 60 days after discharge. Investigators were blinded to assignment, but patients and practice staff were not. Participants included established patients of the study practices who were aged 18 years or older, discharged from an acute care hospitalization, and considered medium to high risk for adverse health events by a health system risk score. All analyses were conducted using an intention-to-treat approach.InterventionPatients in the intervention group received automated check-in text messages from their primary care practice on a tapering schedule for 30 days following discharge. Any needs identified by the automated messaging platform were escalated to practice staff for follow-up via an electronic medical record inbox. Patients in the control group received a standard transitional care management telephone call from their practice within 2 business days of discharge.Main outcomes and measuresThe primary study outcome was any acute care revisit (readmission or emergency department visit) within 30 days of discharge.ResultsOf the 4736 participants, 2824 (59.6%) were female; the mean (SD) age was 65.4 (16.5) years. The mean (SD) length of index hospital stay was 5.5 (7.9) days. A total of 2352 patients were randomized to the intervention arm and 2384 were randomized to the control arm. There were 557 (23.4%) acute care revisits in the control group and 561 (23.9%) in the intervention group within 30 days of discharge (risk ratio, 1.02; 95% CI, 0.92-1.13). Among the patients in the intervention arm, 79.5% answered at least 1 message and 41.9% had at least 1 need identified.Conclusions and relevanceIn this randomized clinical trial of a 30-day postdischarge automated texting program, there was no significant reduction in acute care revisits.Trial registrationClinicalTrials.gov Identifier: NCT05245773.

Project description:ImportancePosthospital contact with a primary care team is an established pillar of safe transitions. The prevailing model of telephone outreach is usually limited in scope and operationally burdensome.ObjectiveTo determine whether a 30-day automated texting program to support primary care patients after hospital discharge is associated with reductions in the use of acute care resources.Design, setting, and participantsThis cohort study used a difference-in-differences approach at 2 academic primary care practices in Philadelphia from January 27 through August 27, 2021. Established patients of the study practices who were 18 years or older, were discharged from an acute care hospitalization, and received the usual transitional care management telephone call were eligible for the study. At the intervention practice, 604 discharges were eligible and 430 (374 patients, of whom 46 had >1 discharge) were enrolled in the intervention. At the control practice, 953 patients met eligibility criteria. The study period, including before and after the intervention, ran from August 27, 2020, through August 27, 2021.ExposurePatients received automated check-in text messages from their primary care practice on a tapering schedule during the 30 days after discharge. Any needs identified by the automated messaging platform were escalated to practice staff for follow-up via an electronic medical record inbox.Main outcomes and measuresThe primary study outcome was any emergency department (ED) visit or readmission within 30 days of discharge. Secondary outcomes included any ED visit or any readmission within 30 days, analyzed separately, and 30- and 60-day mortality. Analyses were based on intention to treat.ResultsA total of 1885 patients (mean [SD] age, 63.2 [17.3] years; 1101 women [58.4%]) representing 2617 discharges (447 before and 604 after the intervention at the intervention practice; 613 before and 953 after the intervention at the control practice) were included in the analysis. The adjusted odds ratio (aOR) for any use of acute care resources after implementation of the intervention was 0.59 (95% CI, 0.38-0.92). The aOR for an ED visit was 0.77 (95% CI, 0.45-1.30) and for a readmission was 0.45 (95% CI, 0.23-0.86). The aORs for death within 30 and 60 days of discharge at the intervention practice were 0.92 (95% CI, 0.23-3.61) and 0.63 (95% CI, 0.21-1.85), respectively.Conclusions and relevanceThe findings of this cohort study suggest that an automated texting program to support primary care patients after hospital discharge was associated with significant reductions in use of acute care resources. This patient-centered approach may serve as a model for improving postdischarge care.

Project description:BackgroundCurrent text mining tools supporting abstract screening in systematic reviews are not widely used, in part because they lack sensitivity and precision. We set out to develop an accessible, semi-automated "workflow" to conduct abstract screening for systematic reviews and other knowledge synthesis methods.MethodsWe adopt widely recommended text-mining and machine-learning methods to (1) process title-abstracts into numerical training data; and (2) train a classification model to predict eligible abstracts. The predicted abstracts are screened by human reviewers for ("true") eligibility, and the newly eligible abstracts are used to identify similar abstracts, using near-neighbor methods, which are also screened. These abstracts, as well as their eligibility results, are used to update the classification model, and the above steps are iterated until no new eligible abstracts are identified. The workflow was implemented in R and evaluated using a systematic review of insulin formulations for type-1 diabetes (14,314 abstracts) and a scoping review of knowledge-synthesis methods (17,200 abstracts). Workflow performance was evaluated against the recommended practice of screening abstracts by 2 reviewers, independently. Standard measures were examined: sensitivity (inclusion of all truly eligible abstracts), specificity (exclusion of all truly ineligible abstracts), precision (inclusion of all truly eligible abstracts among all abstracts screened as eligible), F1-score (harmonic average of sensitivity and precision), and accuracy (correctly predicted eligible or ineligible abstracts). Workload reduction was measured as the hours the workflow saved, given only a subset of abstracts needed human screening.ResultsWith respect to the systematic and scoping reviews respectively, the workflow attained 88%/89% sensitivity, 99%/99% specificity, 71%/72% precision, an F1-score of 79%/79%, 98%/97% accuracy, 63%/55% workload reduction, with 12%/11% fewer abstracts for full-text retrieval and screening, and 0%/1.5% missed studies in the completed reviews.ConclusionThe workflow was a sensitive, precise, and efficient alternative to the recommended practice of screening abstracts with 2 reviewers. All eligible studies were identified in the first case, while 6 studies (1.5%) were missed in the second that would likely not impact the review's conclusions. We have described the workflow in language accessible to reviewers with limited exposure to natural language processing and machine learning, and have made the code available to reviewers.

Project description:670 patients attend Wansbeck General Hospital each year for elective and emergency permanent pacemaker insertion or modification. Elective patients for new devices attend the cardiology department on the day of procedure and are clerked onto the cardiology ward after insertion. Patients are discharged home the following day with a letter typed by a junior doctor. Prior to October 2011, junior doctors were unaware of any guidelines regarding content of discharge letters due to poor accessibility. Vital information such as pacemaker model and indication for implantation were frequently absent from the typed summaries. In October 2011, the cardiology department reviewed the guidelines establishing the information required in all discharge summaries for pacemaker implantation and these guidelines were published on the ward in an easy to follow proforma for any junior doctor typing letters. Eight essential criteria should be included in each letter; date and indication for insertion, pacemaker type, make and model, access route, complications, chest x-ray and device check results, and follow-up details. Finally, a copy of the letter was to be sent to the cardiology department for clinic follow-up. Ten letters were audited prior to the proforma being issued. 0 letters contained 100% of the required information. Main criteria missing in most letters included indication for insertion (50%), make of device (0%), the route of access (10%) and cc to cardiology department (0%). 70 letters were audited in October 2012, one year following introduction of the discharge proforma.100% of letters contained all of the eight essential criteria required by the departmental guidelines. 53% of the letters were also received by the cardiology department. In conclusion, a set of easy to follow guidelines in the form of a published document on the cardiology ward has produced dramatic improvement in the quality of e-discharge letters for patients undergoing permanent pacemaker implantation.

Project description:ImportanceA central tenet of harm reduction and prevention of opioid overdose deaths is the distribution and use of naloxone. Patient-centered methods that investigate naloxone acquisition and carrying can guide opioid overdose education and naloxone distribution efforts.ObjectiveTo assess patients' self-reported naloxone acquisition and carrying after an emergency department (ED) encounter using automated text messaging.Design, setting, and participantsThis cohort study investigated self-reported patient behaviors involving naloxone after ED discharge in a large, urban academic health system in Philadelphia, Pennsylvania. Adult patients who were prescribed or dispensed naloxone and who had a mobile phone number listed in the electronic health record provided informed consent after ED discharge, and data were collected prospectively using text messaging from October 10, 2020, to March 19, 2021. Patients who did not respond to the survey or who opted out were excluded.ExposureAutomated text message-based survey after ED discharge for patients who were prescribed or dispensed naloxone.Main outcomes and measuresThe primary outcome was patient-reported naloxone acquisition, carrying, and use. Descriptive statistics were used to summarize patient demographic characteristics.ResultsOf 205 eligible patients, 41 (20.0%) completed the survey; of those patients, the mean (SD) age was 39.5 (13.7) years, and 21 (51.2%) were women. Fifteen (36.6%) had a personal history of being given naloxone after an overdose. As indicated by the ED record, 27 participants (65.9%) had naloxone dispensed in the ED, and 36 (87.8%) self-reported acquiring naloxone during or after their ED visit. Twenty-four participants (58.5%) were not carrying naloxone in the week before their ED visit. Twenty participants (48.8%) were carrying naloxone after the ED visit, and 27 (65.9%) reported planning to continue carrying naloxone in the future. Of the 24 individuals (58.5%) not carrying naloxone before their ED encounter, 13 (54.2%) reported planning to continue carrying naloxone in the future.Conclusions and relevanceIn this cohort study of adult patients dispensed or prescribed naloxone from the ED, most reported acquiring naloxone on or after discharge. The ED remains a key point of access to naloxone for individuals at high risk of opioid use and overdose, and text messaging could be a method to engage and motivate patient-reported behaviors in enhancing naloxone acquisition and carrying.

Dataset Information

Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters.

Publications

Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters.

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