Dataset Information

Descriptive Usability Study of CirrODS: Clinical Decision and Workflow Support Tool for Management of Patients With Cirrhosis.

ABSTRACT: BACKGROUND:There are gaps in delivering evidence-based care for patients with chronic liver disease and cirrhosis. OBJECTIVE:Our objective was to use interactive user-centered design methods to develop the Cirrhosis Order Set and Clinical Decision Support (CirrODS) tool in order to improve clinical decision-making and workflow. METHODS:Two work groups were convened with clinicians, user experience designers, human factors and health services researchers, and information technologists to create user interface designs. CirrODS prototypes underwent several rounds of formative design. Physicians (n=20) at three hospitals were provided with clinical scenarios of patients with cirrhosis, and the admission orders made with and without the CirrODS tool were compared. The physicians rated their experience using CirrODS and provided comments, which we coded into categories and themes. We assessed the safety, usability, and quality of CirrODS using qualitative and quantitative methods. RESULTS:We created an interactive CirrODS prototype that displays an alert when existing electronic data indicate a patient is at risk for cirrhosis. The tool consists of two primary frames, presenting relevant patient data and allowing recommended evidence-based tests and treatments to be ordered and categorized. Physicians viewed the tool positively and suggested that it would be most useful at the time of admission. When using the tool, the clinicians placed fewer orders than they placed when not using the tool, but more of the orders placed were considered to be high priority when the tool was used than when it was not used. The physicians' ratings of CirrODS indicated above average usability. CONCLUSIONS:We developed a novel Web-based combined clinical decision-making and workflow support tool to alert and assist clinicians caring for patients with cirrhosis. Further studies are underway to assess the impact on quality of care for patients with cirrhosis in actual practice.

SUBMITTER: Garvin JH

PROVIDER: S-EPMC6636234 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Descriptive Usability Study of CirrODS: Clinical Decision and Workflow Support Tool for Management of Patients With Cirrhosis.

Garvin Jennifer Hornung JH Ducom Julie J Matheny Michael M Miller Anne A Westerman Dax D Reale Carrie C Slagle Jason J Kelly Natalie N Beebe Russ R Koola Jejo J Groessl Erik J EJ Patterson Emily S ES Weinger Matthew M Perkins Amy M AM Ho Samuel B SB

JMIR medical informatics 20190703 3

<h4>Background</h4>There are gaps in delivering evidence-based care for patients with chronic liver disease and cirrhosis.<h4>Objective</h4>Our objective was to use interactive user-centered design methods to develop the Cirrhosis Order Set and Clinical Decision Support (CirrODS) tool in order to improve clinical decision-making and workflow.<h4>Methods</h4>Two work groups were convened with clinicians, user experience designers, human factors and health services researchers, and information tec ...[more]

PMID: 31271153

Similar Datasets

Project description:BackgroundDespite growing evidence that deprescribing can improve clinical outcomes, quality of life and reduce the likelihood of adverse drug events, the practice is not widespread, particularly in hospital settings. Clinical risk assessment tools, like the Drug Burden Index (DBI), can help prioritise patients for medication review and prioritise medications to deprescribe, but are not integrated within routine care. The aim of this study was to conduct formative usability testing of a computerised decision support (CDS) tool, based on DBI, to identify modifications required to the tool prior to trialling in practice.MethodsOur CDS tool comprised a DBI MPage in the electronic medical record (clinical workspace) that facilitated review of a patient's DBI and medication list, access to deprescribing resources, and the ability to deprescribe. Two rounds of scenario-based formative usability testing with think-aloud protocol were used. Seventeen end-users participated in the testing, including junior and senior doctors, and pharmacists.ResultsParticipants expressed positive views about the DBI CDS tool but testing revealed a number of clear areas for improvement. These primarily related to terminology used (i.e. what is a DBI and how is it calculated?), and consistency of functionality and display. A key finding was that users wanted the CDS tool to look and function in a similar way to other decision support tools in the electronic medical record. Modifications were made to the CDS tool in response to user feedback.ConclusionUsability testing proved extremely useful for identifying components of our CDS tool that were confusing, difficult to locate or to understand. We recommend usability testing be adopted prior to implementation of any digital health intervention. We hope our revised CDS tool equips clinicians with the knowledge and confidence to consider discontinuation of inappropriate medications in routine care of hospitalised patients. In the next phase of our project, we plan to pilot test the tool in practice to evaluate its uptake and effectiveness in supporting deprescribing in routine hospital care.

Project description:BackgroundWorkflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic.ResultsWe present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture.ConclusionsWe describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform.

Project description:BackgroundSurrogate decision makers of stroke patients are often unprepared to make critical decisions on life-sustaining treatments. We describe the development process and key features for the Understanding Stroke web-based decision support tool.MethodsWe used multiple strategies to develop a patient-centered, tailored decision aid. We began by forming a Patient and Family Advisory Council to provide continuous input to our multidisciplinary team on the development of the tool. Additionally, focus groups consisting of nurses, therapists, social workers, physicians, stroke survivors, and family members reviewed key elements of the tool, including prognostic information, graphical displays, and values clarification exercise. To design the values clarification exercise, we asked focus groups to provide feedback on a list of important activities of daily living. An ordinal prognostic model was developed for ischemic stroke and intracerebral hemorrhage using data taken from the Virtual International Stroke Trials Archive Plus, and incorporated into the tool.ResultsFocus group participants recommended making numeric prognostic information optional due to possible emotional distress. Pie charts were generally favored by participants for graphical presentation of prognostic information, though a horizontal stacked bar chart was also added due to its prevalence in stroke literature. Plain language descriptions of the modified Rankin Scale were created to accompany the prognostic information. A values clarification exercise was developed consisting of a list of 13 situations that may make an individual consider comfort measures only. The final version of the web based tool (which can be viewed on tablets) included the following sections: general introduction to stroke, outcomes (prognostic information and recovery), in-hospital and life-sustaining treatments, decision making and values clarification, post-hospital care, tips for talking to the health care team, and a summary report. Preliminary usability testing received generally favorable feedback.ConclusionWe developed Understanding Stroke, a tailored decision support tool for surrogate decision makers of stroke patients. The tool was well received and will be formally pilot tested in a group of stroke surrogate decision makers.Trial registrationClinicalTrials.gov ( NCT03427645 ).

Project description:BackgroundThe Canadian Computed Tomography (CT) Head Rule, a clinical decision rule designed to safely reduce imaging in minor head injury, has been rigorously validated and implemented, and yet expected decreases in CT were unsuccessful. Recent work has identified empathic care as a key component in decreasing CT overuse. Health information technology can hinder the clinician-patient relationship. Patient-centered decision tools to support the clinician-patient relationship are needed to promote evidence-based decisions.ObjectiveOur objective is to formatively evaluate an electronic tool that not only helps clinicians at the bedside to determine the need for CT use based on the Canadian CT Head Rule but also promotes evidence-based conversations between patients and clinicians regarding patient-specific risk and patients' specific concerns.MethodsUser-centered design with practice-based and participatory decision aid development was used to design, develop, and evaluate patient-centered decision support regarding CT use in minor head injury in the emergency department. User experience and user interface (UX/UI) development involved successive iterations with incremental refinement in 4 phases: (1) initial prototype development, (2) usability assessment, (3) field testing, and (4) beta testing. This qualitative approach involved input from patients, emergency care clinicians, health services researchers, designers, and clinical informaticists at every stage.ResultsThe Concussion or Brain Bleed app is the product of 16 successive iterative revisions in accordance with UX/UI industry design standards. This useful and usable final product integrates clinical decision support with a patient decision aid. It promotes shared use by emergency clinicians and patients at the point of care within the emergency department context. This tablet computer app facilitates evidence-based conversations regarding CT in minor head injury. It is adaptable to individual clinician practice styles. The resultant tool includes a patient injury evaluator based on the Canadian CT Head Rule and provides patient specific risks using pictographs with natural frequencies and cues for discussion about patient concerns.ConclusionsThis tool was designed to align evidence-based practices about CT in minor head injury patients. It establishes trust, empowers active participation, and addresses patient concerns and uncertainty about their condition. We hypothesize that, when implemented, the Concussion or Brain Bleed app will support-not hinder-the clinician-patient relationship, safely reduce CT use, and improve the patient experience of care.

Project description:ObjectivesTo evaluate the translation of a paper high-risk checklist for PICU patients at risk of clinical deterioration to an automated clinical decision support tool.DesignRetrospective, observational cohort study of an automated clinical decision support tool, the PICU Warning Tool, adapted from a paper checklist to predict clinical deterioration events in PICU patients within 24 hours.SettingTwo quaternary care medical-surgical PICUs-The Children's Hospital of Philadelphia and Cincinnati Children's Hospital Medical Center.PatientsThe study included all patients admitted from July 1, 2014, to June 30, 2015, the year prior to the initiation of any focused situational awareness work at either institution.InterventionsWe replicated the predictions of the real-time PICU Warning Tool by retrospectively querying the institutional data warehouse to identify all patients that would have flagged as high-risk by the PICU Warning Tool for their index deterioration.Measurements and main resultsThe primary exposure of interest was determination of high-risk status during PICU admission via the PICU Warning Tool. The primary outcome of interest was clinical deterioration event within 24 hours of a positive screen. The date and time of the deterioration event was used as the index time point. We evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of the performance of the PICU Warning Tool. There were 6,233 patients evaluated with 233 clinical deterioration events experienced by 154 individual patients. The positive predictive value of the PICU Warning Tool was 7.1% with a number needed to screen of 14 patients for each index clinical deterioration event. The most predictive of the individual criteria were elevated lactic acidosis, high mean airway pressure, and profound acidosis.ConclusionsPerformance of a clinical decision support translation of a paper-based tool showed inferior test characteristics. Improved feasibility of identification of high-risk patients using automated tools must be balanced with performance.

Project description:BackgroundAwareness of lung cancer screening remains low in the screening-eligible population, and when patients visit their clinician never having heard of lung cancer screening, engaging in shared decision making to arrive at an informed decision can be a challenge. Therefore, methods to effectively support both patients and clinicians to engage in these important discussions are essential. To facilitate shared decision making about lung cancer screening, effective methods to prepare patients to have these important discussions with their clinician are needed.ObjectiveOur objective is to develop a computer-tailored decision support tool that meets the certification criteria of the International Patient Decision Aid Standards instrument version 4.0 that will support shared decision making in lung cancer screening decisions.MethodsUsing a 3-phase process, we will develop and test a prototype of a computer-tailored decision support tool in a sample of lung cancer screening-eligible individuals. In phase I, we assembled a community advisory board comprising 10 screening-eligible individuals to develop the prototype. In phase II, we recruited a sample of 13 screening-eligible individuals to test the prototype for usability, acceptability, and satisfaction. In phase III, we are conducting a pilot randomized controlled trial (RCT) with 60 screening-eligible participants who have never been screened for lung cancer. Outcomes tested include lung cancer and screening knowledge, lung cancer screening health beliefs (perceived risk, perceived benefits, perceived barriers, and self-efficacy), perception of being prepared to engage in a patient-clinician discussion about lung cancer screening, occurrence of a patient-clinician discussion about lung cancer screening, and stage of adoption for lung cancer screening.ResultsPhases I and II are complete. Phase III is underway. As of July 15, 2017, 60 participants have been enrolled into the study, and have completed the baseline survey, intervention, and first follow-up survey. We expect to have results by December 31, 2017 and to have data analysis completed by March 1, 2018.ConclusionsResults from usability testing indicate that the computer-tailored decision support tool is easy to use, is helpful, and provides a satisfactory experience for the user. At the conclusion of phase III (pilot RCT), we will have preliminary effect sizes to inform a future fully powered RCT on changes in (1) knowledge about lung cancer and screening, (2) perceived risk of lung cancer, (3) perceived benefits of lung cancer screening, (4) perceived barriers to lung cancer screening, (5) self-efficacy for lung cancer screening, and (6) perceptions of being adequately prepared to engage in a discussion with their clinician about lung cancer screening.

Dataset Information

Descriptive Usability Study of CirrODS: Clinical Decision and Workflow Support Tool for Management of Patients With Cirrhosis.

Publications

Descriptive Usability Study of CirrODS: Clinical Decision and Workflow Support Tool for Management of Patients With Cirrhosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets