Clinical Informatics Research Research Articles

A Research Protocol: Mixed-Mode Designs to Optimize Engagement in Clinical Informatics Research.

To introduce a research protocol that utilizes mixed-mode methodology (i.e., delayed concurrent and sequential approaches) to optimize response rates of two surveys being administered to U.S. nursing homes (NHs). This protocol is being employed in a cross-sectional survey to assess for HIT maturity and nurse practitioners (NP) care environments. Survey recruitment from 3,000 NHs will be conducted from June 2023 to July 2025. Respondents included NH administrators evaluating facility-wide HIT and NPs in each NH rating their care environment.

De-identified data quality assessment approaches by data vendors who license data to healthcare and life sciences researchers.

To gain insights into how data vendor companies (DVs), an important source of de-identified/anonymized licensed patient-related data (D/ALD) used in clinical informatics research in life sciences and the pharmaceutical industry, characterize, conduct, and communicate data quality assessments to researcher purchasers of D/ALD. A qualitative study with interviews of DVs executives and decision-makers in data quality assessments (n = 12) and content analysis of interviews transcripts. Data quality, from the perspective of DVs, is characterized by how it is defined, validated, and processed. DVs identify data quality as the main contributor to successful collaborations with life sciences/pharmaceutical research partners. Data quality feedback from clients provides the basis for DVs reviews and inspections of quality processes. DVs value customer interactions, view collaboration, shared common goals, mutual expertise, and communication related to data quality as success factors. Data quality evaluation practices are important. However, no uniform DVs industry standards for data quality assessment were identified. DVs describe their orientation to data quality evaluation as a direct result of not only the complex nature of data sources, but also of techniques, processes, and approaches used to construct data sets. Because real-world data (RWD), eg, patient data from electronic medical records, is used for real-world evidence (RWE) generation, the use of D/ALD will expand and require refinement. The focus on (and rigor in) data quality assessment (particularly in research necessary to make regulatory decisions) will require more structure, standards, and collaboration between DVs, life sciences/pharmaceutical, informaticists, and RWD/RWE policy-making stakeholders.

Interdisciplinary Informatics to Support Primary Care Sentinel Surveillance of Respiratory Infectious Disease Including COVID-19: Donabedian Evaluation.

The Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) is one of Europe's oldest sentinel systems, providing sentinel surveillance since 1967. We report the interdisciplinary informatics required to run such a system. We used the Donabedian framework to describe the interdisciplinary informatics roles that support the structures, processes and outcomes of the RSC. Over the course of the COVID-19 pandemic University, RCGP, information technology specialists, SQL developers, analysts, practice liaison team, network member primary care providers, and their registered patients have nearly quadrupled the size of the RSC from working with 5 million to 19 million peoples pseudonymised health data. We have produced outputs used by the UK Health Security Agency to describe the epidemiology of COVID-19 and report vaccine effectiveness. We have also supported a trial of community-based therapies for COVID-19 and other observational studies. The home of the primary care sentinel surveillance network is with a clinical informatics research group. Interdisciplinary informatics teamwork was required to support primary care sentinel surveillance; such teams can accelerate the scale, scope and digital maturity of surveillance systems as demonstrated by the RSC across the COVID-19 pandemic.

A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources.

The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records

SummaryRobust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.

Suitability of Electronic Health Record Data for Computational Phenotyping of Diabetes Mellitus at Nairobi Hospital, Nairobi City County, Kenya

This research aims to determine the applicability of routine healthcare in clinical informatics research. One of the key areas of research in precision medicine is computational phenotyping from longitudinal Electronic Health Record (EHR) data. The objective of this research was to determine how the interplay of EHR software design, the use of a data dictionary, the process of data collection, and the training and motivation of the human resource involved in the collection and entry of data into the EHR affect the quality of EHR data thus the suitability of such data for utility in computational phenotyping of diabetes mellitus. This research employed a prospective/retrospective study design at the diabetes clinic in Nairobi Hospital. The first source of data was from interviews with 32 staff; nurses, doctors, and health record officers using a referenced peer-reviewed usability questionnaire. Thereafter, a sample of EHR data collected during routine care between January 2012 and December 2016 was also analyzed by looking into the quality of clusters identified in the data using a density-based clustering algorithm and Statistical Package for Social Sciences (SPSS) version 21. Regression analysis shows that software design and the utility of a data dictionary explained 50.7% and 32.3% respectively in the improvement of the suitability of EHR data for computational phenotyping of diabetes mellitus. Also, EHR software was rated useful (82%) in accomplishing users’ daily tasks. However, EHR data were found to be unsuitable for utility in computational phenotyping of diabetes. Despite the fact that 88% of EHR data were clustered as noise, the clustering algorithm identified a total of 23 clusters from the diabetes dataset. However, with improved quality of EHR data, sub-phenotyping tasks would be achievable. This research concludes that the poor quality of EHR data is a result of employees’ unmet intrinsic factors of motivation.

Abstract A107: Disparities in gastric adenocarcinoma and its precursors in South Texas: A secondary analysis of electronic health data from two affiliated institutions

Abstract Gastric (GC) affects U.S. Latinos disproportionately relative to non-Hispanic whites (NHWs), particularly in South Texas (STX). Helicobacter pylori infection (HP) accounts for 90% of cases and is considered the primary GC risk factor. Disparities in GC incidence rates are likely linked to similar disparities in diagnosis and management of precursor conditions, including chronic HP, gastric ulcer (GU) and atrophic gastritis (AG). Preliminary studies found significantly higher HP exposure among Latinos than NHWs in two local populations: 30.3% vs. 9.2% in men participating in a prostate cancer cohort, and 36.7% vs. 16.2% among men and women attending UT Health San Antonio (UTMed) clinics. This study aimed to establish differences in H. pylori-related gastric diagnoses among Latino and NHW patients at UTMed and its affiliated hospital University Health System (UHS). Aggregate data was compiled from a PCORnet CDM data set created from electronic health records sourced from the two health systems and stored at the Clinical Informatics Research Division in the Department of Population Health Sciences at UTMed. Data was selected by using cohort demographic criteria and standard codes (ICD9/10 and LOINC) for diagnostic and laboratory criteria. DBVisualizer Pro was used to write an Oracle SQL script. Results were exported into Microsoft Excel and the file transferred securely via REDCap Send-It. Incidence rate ratios (RR) for all diagnoses were analyzed using STATA. A total of 844,414 records, 54.5% of which were women, were found matching the criteria (age 18 or older, Latino and NHW; Blacks and Asians excluded). These were examined for H. pylori (HP) infection with or without the other GC precursor conditions, as well as gastric adenocarcinoma (GCA), the most common form of GC. Results were stratified by age, gender, ethnicity and health system. Women accounted for most of the diagnoses (67-72%), except for GU at UHS and GCA at both locations. There were no differences in diagnosis rates between health systems, except for GU (Latino UHS patients were 1.49-1.77 times at risk as their UTMed counterparts) and HP (NHW UTMed women were 1.34 times at risk as UHS women). Latinos had higher rates of all diagnoses (p<0.05) except for GU, where there was no difference between ethnic groups and locations. The largest overall disparity was for combined HP+GCA at UHS (RR=3.50; 95% CI 1.37, 10.60; p=0.0037). Latina women largely accounted for this (6.00; 1.34, 55.20; p=0.0074). UHS GCA was the single diagnosis with the greatest disparity (1.81; 1.47, 2.23; p<0.001), followed by UHS HP (1.75; 1.66, 1.85; p<0.001). Latina women were twice as likely to have GCA as their NHW counterparts at both health systems (1.87-2.12; 1.34, 3.02; p<0.001). Subsequent analyses will take into consideration management practices, follow-up and covariates like median income and geographic disparities, as well as incidence rates of gastric lymphomas, including mucosa-associated lymphoid tissue (MALT), the only lymphoma associated with HP. Citation Format: Dorothy A Long Parma, Eric Moffett, Ariel Morales, Amelie G Ramirez. Disparities in gastric adenocarcinoma and its precursors in South Texas: A secondary analysis of electronic health data from two affiliated institutions [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr A107.

Abstract 290: Lessons Learned From Integrated Management Program Advancing Community Treatment Of Atrial Fibrillation (IMPACT-AF): A Pragmatic Clinical Trial Of Computerized Decision Support In Primary Healthcare

Background: Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) was a pragmatic, cluster randomized trial assessing the clinical relevance and effectiveness of a clinical decision support (CDS) tool in the primary care setting of Nova Scotia, Canada. Key challenges encountered included CDS development and implementation (2013-2018), study recruitment (2014-2016) and data analysis (2018 to present). Methods: Clinical and health informatics researchers developed CDS software designed to help primary care providers (PCP) deliver individualized AF patient care based on national guidelines. Features included prioritized automated alerts signaling material changes in patient clinical or biochemical profiles requiring expedited treatment changes. Challenges documented over the trial duration are presented here as lessons learned. Findings: 1) Resources must be allocated for feasibility testing and software development/updates. CDS development took twice as long as projected. Network access and broadband speeds were key impediments to successful uptake. Although modified after pilot testing with an initial cohort of intervention providers, user feedback at study completion suggested the CDS was not sufficiently user-friendly and did not create efficiencies in the clinical management of AF for patients (i.e., repetitive alerts). 2) Integration across e-platforms is crucial. Intellectual property and other technical issues prohibited integration of the CDS within providers’ existing electronic medical records and desired provincial e-health platforms. PCPs cited double data entry/login as impediments to participation/reasons for withdraw. Challenges with data integration across platforms prevented facile and timely data access, analysis and reporting. 3) Study recruitment is resource intensive. In total, 204 PCPs and 1,204 patients participated, representing 25% of all eligible PCPs and 13% of all persons living with AF in Nova Scotia, respectively. The most effective PCP recruitment strategy was in-office, small group lunch and learns. PCPs with past research experience/those who led patient consent processes were top patient recruiters. The study office played a pivotal role in achieving recruitment targets. Conclusions: A rapid growth in healthcare data is leading to the widespread development of CDS software to analyse it. Our experience found practical issues to address if such applications are to succeed. CDS tools that fully integrate multiple critical co-morbid guideline recommendations across eHealth platforms should be pursued. Feasibility testing to assess the practical utility of any healthcare CDS software prior to its implementation is recommended. Lastly, adequate resources are necessary to support successful recruitment of PCPs for future pragmatic clinical trials.

Visualizing Benefits: Evaluating Healthcare Information System Using IS-Impact Model

Reducing costs and optimizing operations are major challenges in many large-scale organizations including healthcare authorities. Research shows that despite ongoing investments in healthcare information systems (IS), the promised benefits often are partially realized or not at all. Part of the solution is to ensure that evaluation methodologies are available to clearly identify the success of these initiatives and from here articulate and mitigate the deficiencies or move to an alternative technology. The literature asserts that few practitioners have implemented a standardized evaluation approach. Using an established model, namely Information System Impact ( IS-impact ) model, we propose a modified evaluation model to assess and a visualization tool to visualize the success of an information systems from a healthcare perspective. The modified IS-Impact model includes six constructs – – individual impact , organization impact , provincial alignment impact , system quality , information quality , and service quality . We applied the modified IS-impact model and the proposed visualization tool against an existing healthcare software solution. An empirical study was conducted at a healthcare authority, with responses from 150 participants who use the healthcare IS, which confirmed that the proposed model and the visualization tool are valid and reliable to measure healthcare systems success. The evaluation model and the visualization tool are found to be efficient to narrow down the scope of inquiry from the general to the specific and quickly identifying the gaps and successes within the established software solution for the healthcare authority. Healthcare or clinical informatics researchers will be benefited from this research in evaluating the ongoing or nearly established healthcare information systems.

Natural Language Processing utilization in Healthcare

The significance of consolidating Natural Language Processing (NLP) techniques in clinical informatics research has been progressively perceived over the previous years, and has prompted transformative advances. Ordinarily, clinical NLP frameworks are created and assessed on word, sentence, or record level explanations that model explicit traits and highlights, for example, archive content (e.g., persistent status, or report type), record segment types (e.g., current meds, past restorative history, or release synopsis), named substances and ideas (e.g., analyses, side effects, or medicines) or semantic qualities (e.g., nullification, seriousness, or fleetingness). While some NLP undertakings consider expectations at the individual or gathering client level, these assignments still establish a minority. Here we give an expansive synopsis and layout of the difficult issues engaged with characterizing suitable natural and outward assessment strategies for NLP look into that will be utilized for clinical results research, and the other way around. A specific spotlight is set on psychological wellness investigate, a zone still generally understudied by the clinical NLP look into network, however where NLP techniques are of prominent importance. Ongoing advances in clinical NLP strategy improvement have been huge, yet we propose more accentuation should be put on thorough assessment for the field to progress further. To empower this, we give noteworthy recommendations, including an insignificant convention that could be utilized when announcing clinical NLP strategy improvement and its assessment.

Multi-Institutional, Large-Scale, International Applied Clinical Informatics Research Through the Clinical Informatics Research Collaborative (CIRCLE).

There is a critical need need for multi-institutional, large-scale, international applied clinical informatics research, given the global, widespread use of commercially-available electronic health records with different designs, capabilities, configurations, and implementation strategies. The Clinical Informatics Research Collaborative (CIRCLE) aims to identify and develop best practices for safe and effective health information technology design, development, implementation, use, and evaluation.

Development and Deployment of an Open, Modular, Near-Real-Time Patient Monitor Datastream Conduit Toolkit to Enable Healthcare Multimodal Data Fusion in a Live Emergency Department Setting for Experimental Bedside Clinical Informatics Research

Healthcare providers rely on complex biomedical devices to assess, treat, and monitor patients. Ongoing research efforts are attempting to generate and implement better algorithms and mechanisms to ensure the early, accurate, automated, and clinically meaningful recognition of patterns and changes in patient health and pathology. Effecting such evolutionary advances in patient monitoring will likely require large collections of high-resolution physiologic parameter datasets from a broad spectrum of patients. As part of a research program to scientifically improve patient monitoring (with a focus on alarm fatigue mitigation), investigators developed the Medical Technology Interface-Open/Research toolkit with modular conduit components that provide the following capabilities: 1) access to select bedside monitor physiologic signals in real-world clinical settings for near-real-time acquisition, storage, and export of high-resolution patient datastreams in a portable format (.json); 2) establishment of a safe, parallel test environment at the bedside for experimental datastream analyses in a research framework. Deployment and interfacing of toolkit elements with off-the-shelf software solutions in a live emergency department setting enabled the construction of a bedside clinical informatics (BCI) research pipeline infrastructure that featured 1) indexing, search/query, and retrieval of datastreams for sophisticated analyses, experimental processing, and algorithm development; and 2) dataset visualization for expert adjudication of datastream interpretability, alarm clinical significance and severity, and experimental algorithm performance. In order to help institute a collaborative biomedical engineering research resource, this article shares details of the active ED BCI data pipeline and presents preliminary examples of ongoing multimodal data fusion applications.

Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances.

The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances.Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e.g., diagnoses, symptoms, or treatments) or semantic attributes (e.g., negation, severity, or temporality).From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient- or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches.Here we provide a broad summary and outline of the challenging issues involved in defining appropriate intrinsic and extrinsic evaluation methods for NLP research that is to be used for clinical outcomes research, and vice versa. A particular focus is placed on mental health research, an area still relatively understudied by the clinical NLP research community, but where NLP methods are of notable relevance. Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation.

Learning health systems need to bridge the ‘two cultures’ of clinical informatics and data science

BackgroundUK health research policy and plans for population health management are predicated upon transformative knowledge discovery from operational ‘Big Data’. Learning health systems require not only data, but feedback loops...

Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record

Clinical informatics researchers depend on the availability of high-quality data from the electronic health record (EHR) to design and implement new methods and systems for clinical practice and research. However, these data are frequently unavailable or present in a format that requires substantial revision. This article reports the results of a review of informatics literature published from 2010 to 2016 that addresses these issues by identifying categories of data content that might be included or revised in the EHR. We used an iterative review process on 1,215 biomedical informatics research articles. We placed them into generic categories, reviewed and refined the categories, and then assigned additional articles, for a total of three iterations. Our process identified eight categories of data content issues: Adverse Events, Clinician Cognitive Processes, Data Standards Creation and Data Communication, Genomics, Medication List Data Capture, Patient Preferences, Patient-reported Data, and Phenotyping. These categories summarize discussions in biomedical informatics literature that concern data content issues restricting clinical informatics research. These barriers to research result from data that are either absent from the EHR or are inadequate (e.g., in narrative text form) for the downstream applications of the data. In light of these categories, we discuss changes to EHR data storage that should be considered in the redesign of EHRs, to promote continued innovation in clinical informatics. Based on published literature of clinical informaticians' reuse of EHR data, we characterize eight types of data content that, if included in the next generation of EHRs, would find immediate application in advanced informatics tools and techniques.

Biomedical informatics advancing the national health agenda: the AMIA 2015 year-in-review in clinical and consumer informatics.

The field of biomedical informatics experienced a productive 2015 in terms of research. In order to highlight the accomplishments of that research, elicit trends, and identify shortcomings at a macro level, a 19-person team conducted an extensive review of the literature in clinical and consumer informatics. The result of this process included a year-in-review presentation at the American Medical Informatics Association Annual Symposium and a written report (see supplemental data). Key findings are detailed in the report and summarized here. This article organizes the clinical and consumer health informatics research from 2015 under 3 themes: the electronic health record (EHR), the learning health system (LHS), and consumer engagement. Key findings include the following: (1) There are significant advances in establishing policies for EHR feature implementation, but increased interoperability is necessary for these to gain traction. (2) Decision support systems improve practice behaviors, but evidence of their impact on clinical outcomes is still lacking. (3) Progress in natural language processing (NLP) suggests that we are approaching but have not yet achieved truly interactive NLP systems. (4) Prediction models are becoming more robust but remain hampered by the lack of interoperable clinical data records. (5) Consumers can and will use mobile applications for improved engagement, yet EHR integration remains elusive.

New Unintended Adverse Consequences of Electronic Health Records.

Although the health information technology industry has made considerable progress in the design, development, implementation, and use of electronic health records (EHRs), the lofty expectations of the early pioneers have not been met. In 2006, the Provider Order Entry Team at Oregon Health & Science University described a set of unintended adverse consequences (UACs), or unpredictable, emergent problems associated with computer-based provider order entry implementation, use, and maintenance. Many of these originally identified UACs have not been completely addressed or alleviated, some have evolved over time, and some new ones have emerged as EHRs became more widely available. The rapid increase in the adoption of EHRs, coupled with the changes in the types and attitudes of clinical users, has led to several new UACs, specifically: complete clinical information unavailable at the point of care; lack of innovations to improve system usability leading to frustrating user experiences; inadvertent disclosure of large amounts of patient-specific information; increased focus on computer-based quality measurement negatively affecting clinical workflows and patient-provider interactions; information overload from marginally useful computer-generated data; and a decline in the development and use of internally-developed EHRs. While each of these new UACs poses significant challenges to EHR developers and users alike, they also offer many opportunities. The challenge for clinical informatics researchers is to continue to refine our current systems while exploring new methods of overcoming these challenges and developing innovations to improve EHR interoperability, usability, security, functionality, clinical quality measurement, and information summarization and display.

Expanding the boundaries of clinical informatics for interdisciplinary systems research

Health-care organizations and the information they exchange are increasingly complex and difficult to understand necessitating fresh methodological approaches on the part of clinical informatics researchers. Looking at the interplay between health-care information technology (HIT), clinical workflow, and the surrounding organization requires some notion of a system and an interdisciplinary orientation toward research. The paper offers practical advice grounded in the systems approach to researchers who wish to view HIT holistically and part of an integrated system. Examples from literature describe the importance of expanding system boundaries, looking for relationships within those boundaries, simplifying complexity, and accounting for social behavior.

Evaluation and management of pediatric hypertensive crises: hypertensive urgency and hypertensive emergencies.

Hypertension (HTN) in the pediatric population is estimated to have a world-wide prevalence of 2%–5%. As with adults, pediatric patients with HTN can present with hypertensive crises include hypertensive urgency and hypertensive emergencies. However, pediatric blood pressure problems have a greater chance of being from secondary causes of HTN, as opposed to primary HTN, than in adults. Thorough evaluation of a child with a hypertensive emergency includes accurate blood pressure readings, complete and focused symptom history, and appropriate past medical, surgical, and family history. Physical exam should include height, weight, four-limb blood pressures, a general overall examination and especially detailed cardiovascular and neurological examinations, including fundoscopic examination. Initial work-up should typically include electrocardiography, chest X-ray, serum chemistries, complete blood count, and urinalysis. Initial management of hypertensive emergencies generally includes the use of intravenous or oral antihypertensive medications, as well as appropriate, typically outpatient, follow-up. Emergency department goals for hypertensive crises are to (1) safely lower blood pressure, and (2) treat/minimize acute end organ damage, while (3) identifying underlying etiology. Intravenous antihypertensive medications are the treatment modality of choice for hypertensive emergencies with the goal of reducing systolic blood pressure by 25% of the original value over an 8-hour period.

Use of RSS Feeds for the Implementation of Clinical Reminder

A computer-based reminder system can help physicians get right information and make right decisions in daily clinical work in time. This study presents a RSS-based Clinical Reminding System (RCRS) designed for reminding clinicians to deal with their varied unfinished clinical works. The RCRS was implemented in a hospital to automatically generate messages for every clinician on the basis of clinical information gathered from the hospital information system (HIS) and send them by RSS feed. In order to allow all physicians to participate in the project, the RCRS was integrated with the Computerized Physician Order Entry (COPE) system to provide messages whenever a clinician logs in the HIS; the connection on screen lets the clinician easily make some response. The system can help clinicians focus on patient care without keeping track of the schedule of clinical chores stored in various systems. Two physicians, also directors from Clinical Informatics Research & Development Center (CIRD) who were appointed as project leaders of the RCRS project who went through the entire development process were chosen as interviewees to obtain a preliminary evaluation of the system. The results show the "Information Content" of this system was suggested to be modified, and "Information Accuracy", "Formats", "Ease of use" and "Timeliness" of the system were appropriate to meet the system design purposes.