The digital scribe.

Current generation electronic health records suffer a number of problems that make them inefficient and associated with poor clinical satisfaction. Digital scribes or intelligent documentation support systems, take advantage of advances in speech recognition, natural language processing and artificial intelligence, to automate the clinical documentation task currently conducted by humans. Whilst in their infancy, digital scribes are likely to evolve through three broad stages. Human led systems task clinicians with creating documentation, but provide tools to make the task simpler and more effective, for example with dictation support, semantic checking and templates. Mixed-initiative systems are delegated part of the documentation task, converting the conversations in a clinical encounter into summaries suitable for the electronic record. Computer-led systems are delegated full control of documentation and only request human interaction when exceptions are encountered. Intelligent clinical environments permit such augmented clinical encounters to occur in a fully digitised space where the environment becomes the computer. Data from clinical instruments can be automatically transmitted, interpreted using AI and entered directly into the record. Digital scribes raise many issues for clinical practice, including new patient safety risks. Automation bias may see clinicians automatically accept scribe documents without checking. The electronic record also shifts from a human created summary of events to potentially a full audio, video and sensor record of the clinical encounter. Digital scribes promisingly offer a gateway into the clinical workflow for more advanced support for diagnostic, prognostic and therapeutic tasks

Integrated care and the working record

By default, many discussions and specifications of electronic health records or integrated care records often conceptualize the record as a passive information repository. This article presents data from a case study of work in a medical unit in a major metropolitan hospital. It shows how the clinicians tailored, re-presented and augmented clinical information to support their own roles in the delivery of care for individual patients. This is referred to as the working record: a set of complexly interrelated clinician-centred documents that are locally evolved, maintained and used to support delivery of care in conjunction with the more patient-centred chart that will be stored in the medical records department on the patient’s discharge. Implications are drawn for how an integrated care record could support the local tailorability and flexibility that underpin this working record and hence underpin practice

Challenges of developing a digital scribe to reduce clinical documentation burden.

Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms

Automation bias in electronic prescribing

© 2017 The Author(s). Background: Clinical decision support (CDS) in e-prescribing can improve safety by alerting potential errors, but introduces new sources of risk. Automation bias (AB) occurs when users over-rely on CDS, reducing vigilance in information seeking and processing. Evidence of AB has been found in other clinical tasks, but has not yet been tested with e-prescribing. This study tests for the presence of AB in e-prescribing and the impact of task complexity and interruptions on AB. Methods: One hundred and twenty students in the final two years of a medical degree prescribed medicines for nine clinical scenarios using a simulated e-prescribing system. Quality of CDS (correct, incorrect and no CDS) and task complexity (low, low + interruption and high) were varied between conditions. Omission errors (failure to detect prescribing errors) and commission errors (acceptance of false positive alerts) were measured. Results: Compared to scenarios with no CDS, correct CDS reduced omission errors by 38.3% (p < .0001, n = 120), 46.6% (p < .0001, n = 70), and 39.2% (p < .0001, n = 120) for low, low + interrupt and high complexity scenarios respectively. Incorrect CDS increased omission errors by 33.3% (p < .0001, n = 120), 24.5% (p < .009, n = 82), and 26.7% (p < .0001, n = 120). Participants made commission errors, 65.8% (p < .0001, n = 120), 53.5% (p < .0001, n = 82), and 51.7% (p < .0001, n = 120). Task complexity and interruptions had no impact on AB. Conclusions: This study found evidence of AB omission and commission errors in e-prescribing. Verification of CDS alerts is key to avoiding AB errors. However, interventions focused on this have had limited success to date. Clinicians should remain vigilant to the risks of CDS failures and verify CDS

Review of "Biomedical Informatics; Computer Applications in Health Care and Biomedicine" by Edward H. Shortliffe and James J. Cimino

This article is an invited review of the third edition of "Biomedical Informatics; Computer Applications in Health Care and Biomedicine", one of thirty-six volumes in Springer's 'Health Informatics Series', edited by E. Shortliffe and J. Cimino. This book spans most of the current methods and issues in health informatics, ranging through subjects as varied as data acquisition and storage, standards, natural language processing, imaging, electronic health records, decision support, teaching methods and ethics. The book is aimed at 'healthcare professionals', and is certainly appropriate for the non-technical informatics user. However, this book is also excellent background reading for the technical engineer who may be interested in the possible problems that confront the users in this field

Using multiclass classification to automate the identification of patient safety incident reports by type and severity

Background: Approximately 10% of admissions to acute-care hospitals are associated with an adverse event. Analysis of incident reports helps to understand how and why incidents occur and can inform policy and practice for safer care. Unfortunately our capacity to monitor and respond to incident reports in a timely manner is limited by the sheer volumes of data collected. In this study, we aim to evaluate the feasibility of using multiclass classification to automate the identification of patient safety incidents in hospitals. Methods: Text based classifiers were applied to identify 10 incident types and 4 severity levels. Using the one-versus-one (OvsO) and one-versus-all (OvsA) ensemble strategies, we evaluated regularized logistic regression, linear support vector machine (SVM) and SVM with a radial-basis function (RBF) kernel. Classifiers were trained and tested with “balanced” datasets (n_ Type  = 2860, n_ SeverityLevel  = 1160) from a state-wide incident reporting system. Testing was also undertaken with imbalanced “stratified” datasets (n_ Type  = 6000, n_ SeverityLevel =5950) from the state-wide system and an independent hospital reporting system. Classifier performance was evaluated using a confusion matrix, as well as F-score, precision and recall. Results: The most effective combination was a OvsO ensemble of binary SVM RBF classifiers with binary count feature extraction. For incident type, classifiers performed well on balanced and stratified datasets (F-score: 78.3, 73.9%), but were worse on independent datasets (68.5%). Reports about falls, medications, pressure injury, aggression and blood products were identified with high recall and precision. “Documentation” was the hardest type to identify. For severity level, F-score for severity assessment code (SAC) 1 (extreme risk) was 87.3 and 64% for SAC4 (low risk) on balanced data. With stratified data, high recall was achieved for SAC1 (82.8–84%) but precision was poor (6.8–11.2%). High risk incidents (SAC2) were confused with medium risk incidents (SAC3). Conclusions: Binary classifier ensembles appear to be a feasible method for identifying incidents by type and severity level. Automated identification should enable safety problems to be detected and addressed in a more timely manner. Multi-label classifiers may be necessary for reports that relate to more than one incident type.Ying Wang, Enrico Coiera, William Runciman and Farah Magrab

A network model of activities in primary care consultations.

OBJECTIVE:The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods. MATERIALS AND METHODS:This is an observational study in Australian general practice involving 31 consultations with 4 primary care physicians. Consultations were audio-recorded, and computer interactions were recorded using screen capture. Physical interactions in consultation rooms were noted by observers. Brief interviews were conducted after consultations. Conversational transcripts were analyzed to identify different activities and their speech content as well as verbal cues signaling activity transitions. An activity transition analysis was then undertaken to generate a network of activities and transitions. RESULTS:Observed activity classes followed those described in well-known primary care consultation models. Activities were often fragmented across consultations, did not flow necessarily in a defined order, and the flow between activities was nonlinear. Modeling activities as a network revealed that discussing a patient's present complaint was the most central activity and was highly connected to medical history taking, physical examination, and assessment, forming a highly interrelated bundle. Family history, allergy, and investigation discussions were less connected suggesting less dependency on other activities. Clear verbal signs were often identifiable at transitions between activities. DISCUSSION:Primary care consultations do not appear to follow a classic linear model of defined information seeking activities; rather, they are fragmented, highly interdependent, and can be reactively triggered. CONCLUSION:The nonlinearity of activities has significant implications for the design of automated information capture. Whereas dictation systems generate literal translation of speech into text, speech-based clinical summary systems will need to link disparate information fragments, merge their content, and abstract coherent information summaries

Conceptual Challenges for Advancing the Socio-Technical Underpinnings of Health Informatics

This discussion paper considers the adoption of socio-technical perspectives and their theoretical and practical influence within the discipline of health informatics. The paper highlights the paucity of discussion of the philosophy, theory and concepts of socio-technical perspectives within health informatics. Instead of a solid theoretical base from which to describe, study and understand human-information technology interactions we continue to have fragmented, unelaborated understandings. This has resulted in a continuing focus on technical system performance and increasingly managerial outputs to the detriment of social and technical systems analysis. It has also limited critical analyses and the adaptation of socio-technical approaches beyond the immediate environment to the broader social systems of contemporary society, an expansion which is increasingly mandated in today’s complex health environment

How Patient Work Changes Over Time for People With Multimorbid Type 2 Diabetes: Qualitative Study.

BACKGROUND: The experiences of patients change throughout their illness trajectory and differ according to their medical history, but digital support tools are often designed for one specific moment in time and do not change with the patient as their health state changes. This presents a fragmented support pattern where patients have to move from one app to another as they move between health states, and some subpopulations of patients do not have their needs addressed at all. OBJECTIVE: This study aims to investigate how patient work evolves over time for those living with type 2 diabetes mellitus and chronic multimorbidity, and explore the implications for digital support system design. METHODS: In total, 26 patients with type 2 diabetes mellitus and chronic multimorbidity were recruited. Each interview was conducted twice, and interviews were transcribed and analyzed according to the Chronic Illness Trajectory Model. RESULTS: Four unique illness trajectories were identified with different patient work goals and needs: living with stable chronic conditions involves patients seeking to make patient work as routinized and invisible as possible; dealing with cycles of acute or crisis episodes included heavily multimorbid patients who sought support with therapy adherence; responding to unstable changes described patients currently experiencing rapid health changes and increasing patient work intensity; and coming back from crisis focused on patients coping with a loss of normalcy. CONCLUSIONS: Patient work changes over time based on the experiences of the individual, and its timing and trajectory need to be considered when designing digital support interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2018-022163