Opportunities for Electronic Health Record Data to Support Business Functions in the Pharmaceutical Industry—A Case Study from Pfizer,Inc.

The Pfizer Healthcare Informatics team conducted a series of guided interviews with 35 Pfizer senior leaders to elicit their understanding, desires, and expectations of how Electronic Health Records (EHR) might be used in the pharmaceutical industry today and/or in the future. The interviews yielded fourteen use case categories comprising 42 specific use cases. The highest priority use cases were “Drug Safety & Surveillance,” “Clinical Trial Recruitment,” and “Support Regulatory Approval.” Fifteen EHR companies were surveyed to assess their functionality against the specified use cases. Self-reported responses from the EHR companies were highest for “Virtual Phase IV Trials” and “Document Management for Clinical Trials.” This research identifies preliminary opportunities for EHR products to provide aggregate, blinded data to address the interests of the pharmaceutical industry. However, further collaboration between the stakeholders will be necessary to ensure the full realization of the opportunities for data re-use.     

Building longitudinal medication dose data using medication information extracted from clinical notes in electronic health records

ObjectiveTo develop an algorithm for building longitudinal medication dose datasets using information extracted from clinical notes in electronic health records (EHRs).Materials and MethodsWe developed an algorithm that converts medication information extracted using natural language processing (NLP) into a usable format and builds longitudinal medication dose datasets. We evaluated the algorithm on 2 medications extracted from clinical notes of Vanderbilt’s EHR and externally validated the algorithm using clinical notes from the MIMIC-III clinical care database.ResultsFor the evaluation using Vanderbilt’s EHR data, the performance of our algorithm was excellent; F1-measures were ≥0.98 for both dose intake and daily dose. For the external validation using MIMIC-III, the algorithm achieved F1-measures ≥0.85 for dose intake and ≥0.82 for daily dose.DiscussionOur algorithm addresses the challenge of building longitudinal medication dose data using information extracted from clinical notes. Overall performance was excellent, but the algorithm can perform poorly when incorrect information is extracted by NLP systems. Although it performed reasonably well when applied to the external data source, its performance was worse due to differences in the way the drug information was written. The algorithm is implemented in the R package, “EHR,” and the extracted data from Vanderbilt’s EHRs along with the gold standards are provided so that users can reproduce the results and help improve the algorithm.ConclusionOur algorithm for building longitudinal dose data provides a straightforward way to use EHR data for medication-based studies. The external validation results suggest its potential for applicability to other systems.     

Brief communication: Changing the research landscape: the New York City Clinical Data Research Network

The New York City Clinical Data Research Network (NYC-CDRN), funded by the Patient-Centered Outcomes Research Institute (PCORI), brings together 22 organizations including seven independent health systems to enable patient-centered clinical research, support a national network, and facilitate learning healthcare systems. The NYC-CDRN includes a robust, collaborative governance and organizational infrastructure, which takes advantage of its participants’ experience, expertise, and history of collaboration. The technical design will employ an information model to document and manage the collection and transformation of clinical data, local institutional staging areas to transform and validate data, a centralized data processing facility to aggregate and share data, and use of common standards and tools. We strive to ensure that our project is patient-centered; nurtures collaboration among all stakeholders; develops scalable solutions facilitating growth and connections; chooses simple, elegant solutions wherever possible; and explores ways to streamline the administrative and regulatory approval process across sites.     

Identifying and prioritizing benefits and risks of using privacy-enhancing software through participatory design: a nominal group technique study with patients living with chronic conditions

ObjectiveWhile patients often contribute data for research, they want researchers to protect their data. As part of a participatory design of privacy-enhancing software, this study explored patients’ perceptions of privacy protection in research using their healthcare data. Materials and MethodsWe conducted 4 focus groups with 27 patients on privacy-enhancing software using the nominal group technique. We provided participants with an open source software prototype to demonstrate privacy-enhancing features and elicit privacy concerns. Participants generated ideas on benefits, risks, and needed additional information. Following a thematic analysis of the results, we deployed an online questionnaire to identify consensus across all 4 groups. Participants were asked to rank-order benefits and risks. Themes around “needed additional information” were rated by perceived importance on a 5-point Likert scale.ResultsParticipants considered “allowance for minimum disclosure” and “comprehensive privacy protection that is not currently available” as the most important benefits when using the privacy-enhancing prototype software. The most concerning perceived risks were “additional checks needed beyond the software to ensure privacy protection” and the “potential of misuse by authorized users.” Participants indicated a desire for additional information with 6 of the 11 themes receiving a median participant rating of “very necessary” and rated “information on the data custodian” as “essential.”ConclusionsPatients recognize not only the benefits of privacy-enhancing software, but also inherent risks. Patients desire information about how their data are used and protected. Effective patient engagement, communication, and transparency in research may improve patients’ comfort levels, alleviate patients’ concerns, and thus promote ethical research.     

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention—the starting point for delivery of “All the right care, but only the right care,” an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.     

Inviting patients to identify diagnostic concerns through structured evaluation of their online visit notes

BackgroundThe 21st Century Cures Act mandates patients’ access to their electronic health record (EHR) notes. To our knowledge, no previous work has systematically invited patients to proactively report diagnostic concerns while documenting and tracking their diagnostic experiences through EHR-based clinician note review.ObjectiveTo test if patients can identify concerns about their diagnosis through structured evaluation of their online visit notes.MethodsIn a large integrated health system, patients aged 18–85 years actively using the patient portal and seen between October 2019 and February 2020 were invited to respond to an online questionnaire if an EHR algorithm detected any recent unexpected return visit following an initial primary care consultation (“at-risk” visit). We developed and tested an instrument (Safer Dx Patient Instrument) to help patients identify concerns related to several dimensions of the diagnostic process based on notes review and recall of recent “at-risk” visits. Additional questions assessed patients’ trust in their providers and their general feelings about the visit. The primary outcome was a self-reported diagnostic concern. Multivariate logistic regression tested whether the primary outcome was predicted by instrument variables.ResultsOf 293 566 visits, the algorithm identified 1282 eligible patients, of whom 486 responded. After applying exclusion criteria, 418 patients were included in the analysis. Fifty-one patients (12.2%) identified a diagnostic concern. Patients were more likely to report a concern if they disagreed with statements “the care plan the provider developed for me addressed all my medical concerns” [odds ratio (OR), 2.65; 95% confidence interval [CI], 1.45–4.87) and “I trust the provider that I saw during my visit” (OR, 2.10; 95% CI, 1.19–3.71) and agreed with the statement “I did not have a good feeling about my visit” (OR, 1.48; 95% CI, 1.09–2.01).ConclusionPatients can identify diagnostic concerns based on a proactive online structured evaluation of visit notes. This surveillance strategy could potentially improve transparency in the diagnostic process.     

Brief communication: Louisiana Clinical Data Research Network: establishing an infrastructure for efficient conduct of clinical research

The state of Louisiana, like the nation as a whole, is facing the salient challenge of improving population health and efficiency of healthcare delivery. Research to inform innovations in healthcare will best enhance this effort if it is timely, efficient, and patient-centered. The Louisiana Clinical Data Research Network (LACDRN) will increase the capacity to conduct robust comparative effectiveness research by building a health information technology infrastructure that provides access to comprehensive clinical data for more than 1 million patients statewide. To ensure that network-based research best serves its end-users, the project will actively engage patients and providers as key informants and decision-makers in the implementation of LACDRN. The network''s patient-centered research agenda will prioritize patients’ and clinicians’ needs and aim to support evidence-based decisions on the healthcare they receive and provide, to optimize patient outcomes and quality of life.     

Research Integrated Network of Systems (RINS): a virtual data warehouse for the acceleration of translational research

ObjectiveIntegrated, real-time data are crucial to evaluate translational efforts to accelerate innovation into care. Too often, however, needed data are fragmented in disparate systems. The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) developed and implemented a universal study identifier—the Research Master Identifier (RMID)—for tracking research studies across disparate systems and a data warehouse-inspired model—the Research Integrated Network of Systems (RINS)—for integrating data from those systems.Materials and MethodsIn 2017, MUSC began requiring the use of RMIDs in informatics systems that support human subject studies. We developed a web-based tool to create RMIDs and application programming interfaces to synchronize research records and visualize linkages to protocols across systems. Selected data from these disparate systems were extracted and merged nightly into an enterprise data mart, and performance dashboards were created to monitor key translational processes.ResultsWithin 4 years, 5513 RMIDs were created. Among these were 726 (13%) bridged systems needed to evaluate research study performance, and 982 (18%) linked to the electronic health records, enabling patient-level reporting.DiscussionBarriers posed by data fragmentation to assessment of program impact have largely been eliminated at MUSC through the requirement for an RMID, its distribution via RINS to disparate systems, and mapping of system-level data to a single integrated data mart.ConclusionBy applying data warehousing principles to federate data at the “study” level, the RINS project reduced data fragmentation and promoted research systems integration.     

科研型电子健康档案系统发展与展望

随着越来越多的医疗机构开始应用电子健康档案系统（Electronic Health Records,EHR）来管理患者资料,基于在临床研究工作对患者资料的需求,各研究机构也开始以电子健康档案系统作为临床研究的数据来源。EHRCR（Electronic Health Records／Clinical Research）项目是在2006年12月由HL7技术委员会（Health Level Seven Technical Committee,HL7TC）和欧洲健康档案研究所（European Institute for Health Records,EuroRec）发起,旨在研究未来可以支持临床研究的电子健康档案系统应具有的功能,以及与此相关的系统、网络和业务流程。因此,对该项目的最新研究成果加以介绍,作为我国电子健康档案行业发展的参考。     

Wickedness or folly? The ethics of NICE's decisions

A rebuttal is provided to each of the arguments adduced by John Harris, an Editor‐in‐Chief of the Journal of Medical Ethics, in two editorials in the journal in support of the view that National Institute for Health and Clinical Excellence's procedures and methods for making recommendations about healthcare procedures for use in the National Health Service in England and Wales are the product of “wickedness or folly or more likely both”, “ethically illiterate as well as socially divisive”, responsible for the “perversion of science as well as of morality” and are “contrary to basic morality and contrary to human rights”.     

An atomic approach to the design and implementation of a research data warehouse

ObjectiveAs a long-standing Clinical and Translational Science Awards (CTSA) Program hub, the University of Pittsburgh and the University of Pittsburgh Medical Center (UPMC) developed and implemented a modern research data warehouse (RDW) to efficiently provision electronic patient data for clinical and translational research.Materials and MethodsWe designed and implemented an RDW named Neptune to serve the specific needs of our CTSA. Neptune uses an atomic design where data are stored at a high level of granularity as represented in source systems. Neptune contains robust patient identity management tailored for research; integrates patient data from multiple sources, including electronic health records (EHRs), health plans, and research studies; and includes knowledge for mapping to standard terminologies.ResultsNeptune contains data for more than 5 million patients longitudinally organized as Health Insurance Portability and Accountability Act (HIPAA) Limited Data with dates and includes structured EHR data, clinical documents, health insurance claims, and research data. Neptune is used as a source for patient data for hundreds of institutional review board-approved research projects by local investigators and for national projects.DiscussionThe design of Neptune was heavily influenced by the large size of UPMC, the varied data sources, and the rich partnership between the University and the healthcare system. It includes several unique aspects, including the physical warehouse straddling the University and UPMC networks and management under an HIPAA Business Associates Agreement.ConclusionWe describe the design and implementation of an RDW at a large academic healthcare system that uses a distinctive atomic design where data are stored at a high level of granularity.     

Patient characteristics associated with venous thromboembolic events: a cohort study using pooled electronic health record data

Objective

To demonstrate the potential of de-identified clinical data from multiple healthcare systems using different electronic health records (EHR) to be efficiently used for very large retrospective cohort studies.

Materials and methods

Data of 959 030 patients, pooled from multiple different healthcare systems with distinct EHR, were obtained. Data were standardized and normalized using common ontologies, searchable through a HIPAA-compliant, patient de-identified web application (Explore; Explorys Inc). Patients were 26 years or older seen in multiple healthcare systems from 1999 to 2011 with data from EHR.

Results

Comparing obese, tall subjects with normal body mass index, short subjects, the venous thromboembolic events (VTE) OR was 1.83 (95% CI 1.76 to 1.91) for women and 1.21 (1.10 to 1.32) for men. Weight had more effect then height on VTE. Compared with Caucasian, Hispanic/Latino subjects had a much lower risk of VTE (female OR 0.47, 0.41 to 0.55; male OR 0.24, 0.20 to 0.28) and African-Americans a substantially higher risk (female OR 1.83, 1.76 to 1.91; male OR 1.58, 1.50 to 1.66). This 13-year retrospective study of almost one million patients was performed over approximately 125 h in 11 weeks, part time by the five authors.

Discussion

As research informatics tools develop and more clinical data become available in EHR, it is important to study and understand unique opportunities for clinical research informatics to transform the scale and resources needed to perform certain types of clinical research.

Conclusions

With the right clinical research informatics tools and EHR data, some types of very large cohort studies can be completed with minimal resources.     

A decade post-HITECH: Critical access hospitals have electronic health records but struggle to keep up with other advanced functions

ObjectiveDespite broad electronic health record (EHR) adoption in U.S. hospitals, there is concern that an “advanced use” digital divide exists between critical access hospitals (CAHs) and non-CAHs. We measured EHR adoption and advanced use over time to analyzed changes in the divide.Materials and MethodsWe used 2008 to 2018 American Hospital Association Information Technology survey data to update national EHR adoption statistics. We stratified EHR adoption by CAH status and measured advanced use for both patient engagement (PE) and clinical data analytics (CDA) domains. We used a linear probability regression for each domain with year-CAH interactions to measure temporal changes in the relationship between CAH status and advanced use.ResultsIn 2018, 98.3% of hospitals had adopted EHRs; there were no differences by CAH status. A total of 58.7% and 55.6% of hospitals adopted advanced PE and CDA functions, respectively. In both domains, CAHs were less likely to be advanced users: 46.6% demonstrated advanced use for PE and 32.0% for CDA. Since 2015, the advanced use divide has persisted for PE and widened for CDA.DiscussionEHR adoption among hospitals is essentially ubiquitous; however, CAHs still lag behind in advanced use functions critical to improving care quality. This may be rooted in different advanced use needs among CAH patients and lack of access to technical expertise.ConclusionsThe advanced use divide prevents CAH patients from benefitting from a fully digitized healthcare system. To close the widening gap in CDA, policymakers should consider partnering with vendors to develop implementation guides and standards for functions like dashboards and high-risk patient identification algorithms to better support CAH adoption.     

Tasks as needs: reframing the paradigm of clinical natural language processing research for real-world decision support

Electronic medical records are increasingly used to store patient information in hospitals and other clinical settings. There has been a corresponding proliferation of clinical natural language processing (cNLP) systems aimed at using text data in these records to improve clinical decision-making, in comparison to manual clinician search and clinical judgment alone. However, these systems have delivered marginal practical utility and are rarely deployed into healthcare settings, leading to proposals for technical and structural improvements. In this paper, we argue that this reflects a violation of Friedman’s “Fundamental Theorem of Biomedical Informatics,” and that a deeper epistemological change must occur in the cNLP field, as a parallel step alongside any technical or structural improvements. We propose that researchers shift away from designing cNLP systems independent of clinical needs, in which cNLP tasks are ends in themselves—“tasks as decisions”—and toward systems that are directly guided by the needs of clinicians in realistic decision-making contexts—“tasks as needs.” A case study example illustrates the potential benefits of developing cNLP systems that are designed to more directly support clinical needs.     

Big biomedical data as the key resource for discovery science

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an “-ome to home” approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center’s computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson’s and Alzheimer’s.     

Home-visit rehabilitation in a repopulated village after the Fukushima nuclear disaster

Following the evacuation of areas affected by Japan’s 2011 Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, Kawauchi Village was one of the first municipalities repopulated. Although rehabilitation resources were limited, a healthcare facility near the municipality initiated home-visit rehabilitation in 2016. To the best of our knowledge, reports of home-visit rehabilitation in repopulated villages that were evacuated following a nuclear accident are lacking.This article describes a case study of home-visit rehabilitation in Kawauchi Village. The purpose of this study was to explore how users of home-visit rehabilitation services in Kawauchi Village perceive home-visit rehabilitation, and whether it had a positive impact on their daily life. A questionnaire survey was conducted, and their ability to perform activities of daily living was assessed, to understand the living conditions of the visiting-rehabilitation service users.We studied 10 rehabilitation-service users, with a mean age of 86.8 years, who had used the services for an average of 591.4 days. Themes that emerged from the open-ended questionnaire were “established exercise habits and improved physical functions,” “the joy of returning to the village,” “challenges in the mountainous areas” and “changes in relationships due to the earthquake or evacuation.”In conclusion, home-visit rehabilitation was successfully implemented in the repopulated village, and helped maintain the users’ physical functions. This may thus be a viable choice for rehabilitation care in repopulated areas after disasters.     

A STUDY OF METHODS USED FOR DISINFECTION OF ANAESTHETIC EQUIPMENT BETWEEN TWO SURGICAL OPERATIONS AT A LARGE TEACHING HOSPITAL

A prospective study on the growth of bacteria on certain commonly used anaesthetic equipment was undertaken in a large teaching hospital with a view to assess the effectiveness of disinfection/sterilization procedures. Samples for microbiological assessment were drawn by the worker using standardised procedures and tested in the laboratory by a microbiologist, blinded to the type of sample. Criteria for growth positivity was taken as > 25 colony forming units. A total of 90 observations were taken. 30 each for ’before use’, ’after use’ and ’after disinfection’. Overall 54.6% of the equipment showed growth “before use” with maximum growth being seen in Suction catheters (66.6%) and Guedal airways (60.0%). On the other hand, the proportion of equipment showing growth “after use” was quite high (84.6%), with suction catheters and endotracheal tubes showing 90.0% growth each. There was significant difference as regards “before” and “after” use growth on Endotracheal tubes, Guedel airways and Face masks (p < 0.05). Analysis of growth “after” disinfection” revealed that the probability of growth remains as high as 70% in suction catheters (95% CI=54% to 86%) and 60% in laryngoscopes (95% CI=43% to 78%). The study revealed gross inadequacies in methods of disinfection being followed at present.KEY WORDS: Anaesthetic equipment, Disinfection