Combining Unsupervised, Supervised, and Rule-based Algorithms for Text Mining of Electronic Health Records - A Clinical Decision Support System for Identifying and Classifying Allergies of Concern for Anesthesia During Surgery

Undisclosed allergic reactions of patients are a major risk when undertaking surgeries in hospitals. We present our early experience and preliminary findings for a Clinical Decision Support System (CDSS) being developed in a Norwegian Hospital Trust. The system incorporates unsupervised and supervised machine learning algorithms in combination with rule-based algorithms to identify and classify allergies of concern for anesthesia during surgery. Our approach is novel in that it utilizes unsupervised machine learning to analyze large corpora of narratives to automatically build a clinical language model containing words and phrases of which meanings and relative meanings are also learnt. It further implements a semi-automatic annotation scheme for efficient and interactive machine-learning, which to a large extent eliminates the substantial manual annotation (of clinical narratives) effort necessary for the training of supervised algorithms. Validation of system performance was performed through comparing allergies identified by the CDSS with a manual reference standard

The application of medical terminologies to free-text in routine databases using the example of strategies to reduce infant mortality

Hintergrund Die Säuglingssterblichkeitsrate (IMR), ein wichtiger Indikator für die Qualität eines Gesundheitssystems, liegt in Deutschland seit 10 Jahren bei rund 3.5‰. Generische Qualitätsindikatoren (QIs), wie sie seit 2010 in Deutschland verwendet werden, tragen wesentlich zu einem so guten Wert bei, scheinen aber nicht in der Lage zu sein, den IMR weiter zu reduzieren. Die neonatale Sterblichkeitsrate (NMR) trägt zu 65-70% der IMR bei. Der vorgestellte Ansatz schlägt daher eine Einzelfallanalyse neonataler Todesfälle auf der Grundlage von Krankenakten vor. Die meisten elektronischen Krankenakten enthalten noch immer große Mengen an Freitextdaten. Die semantische Auswertung solcher Daten erfordert, dass die Daten mit ausreichenden Klassifizierungen kodiert oder in eine wissensbasierte Datenbank umgewandelt werden. Methodik Die Nordic-Baltic-Classification (NBC) wurde zur Erkennung vermeidbarer neonataler Todesfälle verwendet. Diese Klassifikation wurde auf eine Stichprobe von 1.968 neonatalen Todesfällen angewandt, die über 90% aller neonatalen Todesfälle in Ost-Berlin von 1973 bis 1989 darstellen. Alle Fälle wurden damals von einer speziellen Kommission verschiedener Experten auf der Grundlage der vollständigen perinatalen und klinischen Daten auf ihre Vermeidbarkeit hin analysiert. Der entwickelte Ansatz ermöglicht es, Datenbanken, die über SQL (Structured Query Language) zugänglich sind, direkt über semantische Abfragen zu durchsuchen, ohne dass weitere Transformationen erforderlich sind. Dazu wurden 1.) eine Erweiterung von SQL „Ontology-SQL“ (O-SQL) entwickelt, die es ermöglicht, semantische Ausdrücke zu verwenden, 2.) ein Framework entwickelt, das einen Standardterminologieserver verwendet, um Freitext enthaltende Datenbanktabellen zu annotieren und 3.) ein Parser entwickelt, der O-SQL Ausdrücke in SQL konvertiert, so dass semantische Abfragen direkt an den Datenbankserver weitergeleitet werden können. Ergebnisse Die NBC wurde verwendet, um die Gruppe der Fälle auszuwählen, die ein hohes Vermeidungspotenzial hatten. Die ausgewählte Gruppe stellte 6,0% aller Fälle dar und 60,4% der Fälle innerhalb dieser Gruppe wurden tatsächlich als vermeidbar oder bedingt vermeidbar beurteilt. Die automatische Erkennung von Fehlbildungen ergab einen F1-Wert von 0,94. Darüber hinaus wurde die Verallgemeinerbarkeit des Ansatzes mit verschiedenen semantischen Abfragen nachgewiesen und dessen Güte mit F1-Werten von 0,91 bis 0,98 gemessen. Zusammenfassung Die Ergebnisse zeigen, dass die vorgestellte Methode automatisch anwendbar ist und ein leistungsfähiges und hochsensitives und -spezifisches Werkzeug zur Auswahl potenziell vermeidbarer neonataler Todesfälle und damit zur Unterstützung einer effizienten Einzelfallanalyse darstellt. Die nahtlose Verknüpfung von Ontologien und Standardtechnologien aus dem Datenbankbereich stellt einen wichtigen Bestandteil der unstrukturierten Datenanalyse dar. Die entwickelte Technologie lässt sich problemlos auf aktuelle Daten anwenden und unterstützt das immer wichtiger werdende Feld der translationalen Forschung.Background The infant mortality rate (IMR), a key indicator of the quality of a healthcare system, has remained at approximately 3.5‰ for the past 10 years in Germany. Generic quality indicators (QIs), as used in Germany since 2010, greatly help to ensure such a good value but do not seem to be able to further reduce the IMR. The neonatal mortality rate (NMR) contributes to 65-70% of the IMR. The presented approach therefore proposes single-case analysis of neonatal deaths on base of medical records. Most electronic medical records still contain large amounts of free-text data. Semantic evaluation of such data requires the data to be encoded with sufficient classifications or transformed into a knowledge-based database. Methods The Nordic-Baltic classification (NBC) was used to detect avoidable neonatal deaths. This classification has been applied to a sample of 1,968 neonatal death records, which represent over 90% of all neonatal deaths in East Berlin from 1973 to 1989. All cases were analyzed as to their preventability based on the complete perinatal and clinical data by a special commission of different experts. The developed approach allows databases accessible via SQL (Structured Query Language) to be searched directly through semantic queries without the need for further transformations. Therefore, I) an extension to SQL named Ontology-SQL (O-SQL) that allows to use semantic expressions, II) a framework that uses a standard terminology server to annotate free-text containing database tables and III) a parser that rewrites O-SQL to SQL, so that such queries can be passed to the database server, have been developed. Results The NBC was used to select the group of cases that had a high potential of avoidance. The selected group represented 6.0% of all cases, and 60.4% of the cases within that group were judged avoidable or conditionally avoidable. The automatic detection of malformations showed an F1 score of 0.94. Furthermore, the generability has been proved with different semantic queries and was measured with between 0.91 and 0.98. Conclusion The results show, that the presented method can be applied automatically and is a powerful and highly specific tool for selecting potentially avoidable neonatal deaths and thus for supporting efficient single case analysis. The seamless connection of ontologies and standard technologies from the database field represents an important constituent of unstructured data analysis. The developed technology can be readily applied to current data and supports the increasingly important field of translational research

Defining and Testing EMR Usability: Principles and Proposed Methods of EMR Usability Evaluation and Rating

For more information about the Information Experience Laboratory, visit http://ielab.missouri.edu/Electronic medical record (EMR) adoption rates have been slower than expected in the United States, especially in comparison to other industry sectors and other developed countries. A key reason, aside from initial costs and lost productivity during EMR implementation, is lack of efficiency and usability of EMRs currently available. Achieving the healthcare reform goals of broad EMR adoption and “meaningful use” will require that efficiency and usability be effectively addressed at a fundamental level. We conducted a literature review of usability principles, especially those applicable to EMRs. The key principles identified were simplicity, naturalness, consistency, minimizing cognitive load, efficient interactions, forgiveness and feedback, effective use of language, effective information presentation, and preservation of context. Usability is often mistakenly equated with user satisfaction, which is an oversimplification. We describe methods of usability evaluation, offering several alternative methods for measuring efficiency and effectiveness, including patient safety. We provide samples of objective, repeatable and cost‐efficient test scenarios applicable to evaluating EMR usability as an adjunct to certification, and we discuss rating schema for scoring the results. (42 pages

User Interfaces for Patient-Centered Communication of Health Status and Care Progress

The recent trend toward patients participating in their own healthcare has opened up numerous opportunities for computing research. This dissertation focuses on how technology can foster this participation, through user interfaces to effectively communicate personal health status and care progress to hospital patients. I first characterize the design space for electronic information communication to patients through field studies conducted in multiple hospital settings. These studies utilize a combination of survey instruments, and low- and high-fidelity prototypes, including a document-editing prototype through which users can view and manage clinical data to automatically associate it with progress notes. The prototype, activeNotes, includes the first known techniques supporting clinical information requests directly within a document editor. A usage study with ICU physicians at New York-Presbyterian Hospital (NYP) substantiated our design and revealed how electronic information related to patient status and care progress is derived from a typical Electronic Health Record system. Insights gained from this study informed following studies to understand how to design abstracted, plain-language views suitable for patients. We gauged both patient and physician responses to information display prototypes deployed in patient rooms for a formative study exploring their design. Following my reports on this study, I discuss the design, development and pilot evaluations of a prototype Personal Health Record application providing live, abstracted clinical information for patients at NYP. The portal, evaluated by cardiothoracic surgery patients, is the first of its kind to allow patients to capture and monitor live data related to their care. Patient use of the portal influenced the subsequent design of tools to support users in making sense of online medication information. These tools, designed with nurses and pharmacists and evaluated by cardiothoracic surgery patients at NYP, were developed using topic modeling approaches and text analysis techniques. Embodied in a prototype called Remedy, they enable rapid filtering and comparison of medication-related search results, based on a number of website features and content topics. I conclude by discussing how findings from this series of studies can help shape the ongoing design and development of patient-centered technology

Finding a Cure: The Case for Regulation and Oversight of Electronic Health Record Systems

In the foreseeable future, it is likely that the familiar, paper-based patient medical files will become a thing of the past. On April 26, 24, President George W. Bush announced a plan to ensure that all Americans\u27 health records are computerized within ten years and to establish a National Health Information Network. Many advocates are enthusiastically promoting the adoption of health information technology (HIT) and electronic health record (HER) systems as a means to improve U.S. health care. HER systems often not only serve as record-keeping systems, but also have multiple capabilities, including drug ordering, decision support, alerts concerning patient allergies and potential drug interactions, reminders concerning routine tests, and various treatment management and data analysis tools. Because these capabilities require sophisticated software, significant risks of software failure exist, which can lead to life-threatening medical errors. Thus far, scholars have not provided a comprehensive assessment of the benefits and risks of this complex technology and evaluated the need for careful regulatory oversight akin to that required, in principle, by the FDA for life-critical medical devices. This paper begins to fill that gap. It analyzes HER systems from both legal and technical perspectives and focuses on how the law can be used as a tool to promote HIT. It is the first law journal article to provide an extensive proposal for regulations to maximize the technology\u27s benefits and reliability. We argue that the advantages of HER systems will outweigh their risks only if these systems are developed and maintained with rigorous adherence to best software engineering and medical informatics practices. To ensure that these goals are achieved, regulatory intervention is needed. The paper carefully delineates recommendations that address the questions of who should regulate HER systems and how they should be regulated, including their approval and continual monitoring. It also proposes requirements for several significant features, including decision support mechanisms, audit trails, and interoperability. Because HER systems are safety-critical, the public\u27s health and welfare will depend upon their effective oversight

Finding a Cure: The Case for Regulation and Oversight of Electronic Health Record Systems

In the foreseeable future, it is likely that the familiar, paper-based patient medical files will become a thing of the past. On April 26, 24, President George W. Bush announced a plan to ensure that all Americans\u27 health records are computerized within ten years and to establish a National Health Information Network. Many advocates are enthusiastically promoting the adoption of health information technology (HIT) and electronic health record (HER) systems as a means to improve U.S. health care. HER systems often not only serve as record-keeping systems, but also have multiple capabilities, including drug ordering, decision support, alerts concerning patient allergies and potential drug interactions, reminders concerning routine tests, and various treatment management and data analysis tools. Because these capabilities require sophisticated software, significant risks of software failure exist, which can lead to life-threatening medical errors. Thus far, scholars have not provided a comprehensive assessment of the benefits and risks of this complex technology and evaluated the need for careful regulatory oversight akin to that required, in principle, by the FDA for life-critical medical devices. This paper begins to fill that gap. It analyzes HER systems from both legal and technical perspectives and focuses on how the law can be used as a tool to promote HIT. It is the first law journal article to provide an extensive proposal for regulations to maximize the technology\u27s benefits and reliability. We argue that the advantages of HER systems will outweigh their risks only if these systems are developed and maintained with rigorous adherence to best software engineering and medical informatics practices. To ensure that these goals are achieved, regulatory intervention is needed. The paper carefully delineates recommendations that address the questions of who should regulate HER systems and how they should be regulated, including their approval and continual monitoring. It also proposes requirements for several significant features, including decision support mechanisms, audit trails, and interoperability. Because HER systems are safety-critical, the public\u27s health and welfare will depend upon their effective oversight

Appraisal of free online symptom checkers and applications for self-diagnosis and triage: An Australian evaluation

The internet has impacted society and changed the way companies and individuals operate on a daily basis. Seeking information online via computer or mobile device is common practice. The phrase ‘Google it’ is now part of modern vernacular and is a resource increasingly utilised by young and old alike. Around 80% of Australian’s search health-related information online as it is convenient, cheap, and available 24/7. Symptom checkers are one tool used by consumers to investigate their health issues. Symptom checkers are automated online programs which use computerised algorithms, asking a series of questions to help determine a potential diagnosis and/or provide suitable triage advice. Recent evidence suggests symptom checkers may not work the way they are intended. Inferior or incorrect healthcare information can potentially have serious consequences on the consumer’s wellbeing and may not have the desired effect of directing consumers to the appropriate point of care. This research evaluated the clinical performance of 36 symptom checkers found on websites and smartphone applications that are freely available for use by the Australian general public. Symptom checkers were exposed to 48 clinical vignettes, generating 1858 symptom checker vignette tests (SCVT). Diagnosis was assessed on the inclusion of the correct diagnosis in the first, the top three or top ten differential diagnoses (n = 1,170 SCVT). Triage advice was assessed on whether the triage category recommended was concordant with our assessment (n = 688 SCVT). The correct diagnosis was listed first in 36% (95% CI 31–42) of SCVT, within the top three in 52% (95% CI 47–59) and within the top ten in 58% (95% CI 53–65). Symptom checkers which claimed to utilise artificial intelligence (AI) outperformed non-AI with the first listed diagnosis being accurate in 46% (95% CI 40–57) versus 32% (95% CI 26–38) of SCVT. Individual symptom checker performance varied considerably, with the average rate of correct diagnosis provided first ranging between 12%–-61%. Triage advice provided was concordant with our assessment in 49% (95% CI 44–54) of SCVT. Appropriate triage advice was provided more frequently for emergency care SCVT at 63% (95% CI 52–71) than for non-urgent SCVT at 30% (95% CI 11–39). Symptom checker performance varied considerably in relation to diagnosis. Triage advice was risk-averse, typically recommending more urgent care pathways than necessary. Given this, symptom checkers may not be working to alleviate demand for health services (particularly emergency services) within Australia—counter to marketing materials of some organisations’ symptom checkers. It is important that symptom checkers do not further burden the healthcare system with inappropriate referrals or incorrect care advice. Although, a balance must be struck as avoiding unsuitable triage advice could potentially result in life-threatening consequences for consumers. Nonetheless, the results of this research make clear that the accuracy of diagnosis and triage advice provided from readily available symptom checkers for the Australian public require improvements before everyday consumers can rely entirely on health information provided via these mediums

A.S.P.E.N. Parenteral Nutrition Safety Consensus Recommendations

Parenteral nutrition (PN) serves as an important therapeutic modality that is used in adults, children, and infants for a variety of indications. The appropriate use of this complex therapy aims to maximize clinical benefit while minimizing the potential risks for adverse events. Complications can occur as a result of the therapy and as the result of the PN process. These consensus recommendations are based on practices that are generally accepted to minimize errors with PN therapy, categorized in the areas of PN prescribing, order review and verification, compounding, and administration. These recommendations should be used in conjunction with other A.S.P.E.N. publications, and researchers should consider studying the questions brought forth in this document