scholarly journals Attitudes of Patients and Health Professionals Regarding Screening Algorithms: Qualitative Study

10.2196/17971 ◽  
2021 ◽  
Vol 5 (8) ◽  
pp. e17971
Author(s):  
Christina Oxholm ◽  
Anne-Marie Soendergaard Christensen ◽  
Regina Christiansen ◽  
Uffe Kock Wiil ◽  
Anette Søgaard Nielsen
Keyword(s):  
Health Professionals ◽  
Development Process ◽  
Ad Hoc ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Alcohol Problems ◽  
Close Reading ◽  
Health Records ◽  
Professional Discretion ◽  
Semistructured Interviews

Background As a preamble to an attempt to develop a tool that can aid health professionals at hospitals in identifying whether the patient may have an alcohol abuse problem, this study investigates opinions and attitudes among both health professionals and patients about using patient data from electronic health records (EHRs) in an algorithm screening for alcohol problems. Objective The aim of this study was to investigate the attitudes and opinions of patients and health professionals at hospitals regarding the use of previously collected data in developing and implementing an algorithmic helping tool in EHR for screening inexpedient alcohol habits; in addition, the study aims to analyze how patients would feel about asking and being asked about alcohol by staff, based on a notification in the EHR from such a tool. Methods Using semistructured interviews, we interviewed 9 health professionals and 5 patients to explore their opinions and attitudes about an algorithm-based helping tool and about asking and being asked about alcohol usage when being given a reminder from this type of tool. The data were analyzed using an ad hoc method consistent with a close reading and meaning condensing. Results The health professionals were both positive and negative about a helping tool grounded in algorithms. They were optimistic about the potential of such a tool to save some time by providing a quick overview if it was easy to use but, on the negative side, noted that this type of helping tool might take away the professionals’ instinct. The patients were overall positive about the helping tool, stating that they would find this tool beneficial for preventive care. Some of the patients expressed concerns that the information provided by the tool could be misused. Conclusions When developing and implementing an algorithmic helping tool, the following aspects should be considered: (1) making the helping tool as transparent in its recommendations as possible, avoiding black boxing, and ensuring room for professional discretion in clinical decision making; and (2) including and taking into account the attitudes and opinions of patients and health professionals in the design and development process of such an algorithmic helping tool.

scholarly journals Attitudes of Patients and Health Professionals Regarding Screening Algorithms: Qualitative Study (Preprint)

2020 ◽  
Author(s):  
Christina Oxholm ◽  
Anne-Marie Soendergaard Christensen ◽  
Regina Christiansen ◽  
Uffe Kock Wiil ◽  
Anette Søgaard Nielsen
Keyword(s):  
Health Professionals ◽  
Development Process ◽  
Ad Hoc ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Alcohol Problems ◽  
Close Reading ◽  
Health Records ◽  
Professional Discretion ◽  
Semistructured Interviews

BACKGROUND As a preamble to an attempt to develop a tool that can aid health professionals at hospitals in identifying whether the patient may have an alcohol abuse problem, this study investigates opinions and attitudes among both health professionals and patients about using patient data from electronic health records (EHRs) in an algorithm screening for alcohol problems. OBJECTIVE The aim of this study was to investigate the attitudes and opinions of patients and health professionals at hospitals regarding the use of previously collected data in developing and implementing an algorithmic helping tool in EHR for screening inexpedient alcohol habits; in addition, the study aims to analyze how patients would feel about <i>asking</i> and <i>being asked</i> about alcohol by staff, based on a notification in the EHR from such a tool. METHODS Using semistructured interviews, we interviewed 9 health professionals and 5 patients to explore their opinions and attitudes about an algorithm-based helping tool and about asking and being asked about alcohol usage when being given a reminder from this type of tool. The data were analyzed using an ad hoc method consistent with a close reading and meaning condensing. RESULTS The health professionals were both positive and negative about a helping tool grounded in algorithms. They were optimistic about the potential of such a tool to save some time by providing a quick overview if it was easy to use but, on the negative side, noted that this type of helping tool might take away the professionals’ instinct. The patients were overall positive about the helping tool, stating that they would find this tool beneficial for preventive care. Some of the patients expressed concerns that the information provided by the tool could be misused. CONCLUSIONS When developing and implementing an algorithmic helping tool, the following aspects should be considered: (1) making the helping tool as transparent in its recommendations as possible, avoiding black boxing, and ensuring room for professional discretion in clinical decision making; and (2) including and taking into account the attitudes and opinions of patients and health professionals in the design and development process of such an algorithmic helping tool.

scholarly journals Desiderata for computable representations of electronic health records-driven phenotype algorithms

2015 ◽  
Vol 22 (6) ◽  
pp. 1220-1230 ◽  
Author(s):  
Huan Mo ◽  
William K Thompson ◽  
Luke V Rasmussen ◽  
Jennifer A Pacheco ◽  
Guoqian Jiang ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Language Processing ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Relational Algebra ◽  
Common Data Model ◽  
Health Records ◽  
Electronic Health ◽  
Value Sets ◽  
Text Searching

Abstract Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

scholarly journals Integrating Genomic Resources with Electronic Health Records using the HL7 Infobutton Standard

2016 ◽  
Vol 07 (03) ◽  
pp. 817-831 ◽  
Author(s):  
Casey Overby ◽  
Guilherme Del Fiol ◽  
Wendy Rubinstein ◽  
Donna Maglott ◽  
Tristan Nelson ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Search Interface ◽  
Health Records ◽  
Knowledge Resources ◽  
Genomic Resources ◽  
Full Compliance ◽  
Genomic Knowledge ◽  
Electronic Health

SummaryThe Clinical Genome Resource (ClinGen) Electronic Health Record (EHR) Workgroup aims to integrate ClinGen resources with EHRs. A promising option to enable this integration is through the Health Level Seven (HL7) Infobutton Standard. EHR systems that are certified according to the US Meaningful Use program provide HL7-compliant infobutton capabilities, which can be leveraged to support clinical decision-making in genomics.To integrate genomic knowledge resources using the HL7 infobutton standard. Two tactics to achieve this objective were: (1) creating an HL7-compliant search interface for ClinGen, and (2) proposing guidance for genomic resources on achieving HL7 Infobutton standard accessibility and compliance.We built a search interface utilizing OpenInfobutton, an open source reference implementation of the HL7 Infobutton standard. ClinGen resources were assessed for readiness towards HL7 compliance. Finally, based upon our experiences we provide recommendations for publishers seeking to achieve HL7 compliance.Eight genomic resources and two sub-resources were integrated with the ClinGen search engine via OpenInfobutton and the HL7 infobutton standard. Resources we assessed have varying levels of readiness towards HL7-compliance. Furthermore, we found that adoption of standard terminologies used by EHR systems is the main gap to achieve compliance.Genomic resources can be integrated with EHR systems via the HL7 Infobutton standard using OpenInfobutton. Full compliance of genomic resources with the Infobutton standard would further enhance interoperability with EHR systems. Citation: Heale BSE, Overby CL, Del Fiol G, Rubinstein WS, Maglott DR, Nelson TH, Milosavljevic A, Martin CL, Goehringer SR, Freimuth RR, Williams MS. Integrating genomic resources with electronic health records using the HL7 Infobutton standard.

Goals of palliative cancer therapy.

1993 ◽  
Vol 11 (2) ◽  
pp. 378-381 ◽  
Author(s):  
F Porzsolt ◽  
I Tannock
Keyword(s):  
Quality Of Life ◽  
Decision Making ◽  
Cost Effectiveness ◽  
Cancer Therapy ◽  
Health Professionals ◽  
Palliative Treatment ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Oncology Practice

The major conclusions of the Workshop on Goals of Palliative Cancer Therapy are as follows: 1. The goals of any cancer therapy should be stated explicitly. 2. If the goal of treatment is palliation, this should be documented according to one of the established and validated methods for assessment of quality of life. Several validated methods are available, and although imperfect, have been shown to give reliable information. 3. The use of simple measures of quality of life (eg, symptom checklists, pain assessment cards) should become routine in oncology practice. The act of introducing such measures improves palliation. 4. Measures of cost-effectiveness should be used more widely in clinical decision making to ensure the appropriate deployment of resources. 5. There must be improved education of all health professionals with regard to the multiple methods for provision of palliative treatment to cancer patients and the assessment of palliation.

scholarly journals Convergence of Blockchain, Autonomous Agents, and Knowledge Graph to Share Electronic Health Records

2021 ◽  
Vol 4 ◽  
Author(s):  
Yao Yao ◽  
Meghana Kshirsagar ◽  
Gauri Vaidya ◽  
Jens Ducrée ◽  
Conor Ryan
Keyword(s):  
Electronic Health Records ◽  
Autonomous Agents ◽  
Knowledge Integration ◽  
Clinical Decision Making ◽  
User Authentication ◽  
Clinical Decision ◽  
Knowledge Graph ◽  
Autonomous Agent ◽  
Health Records ◽  
Electronic Health

In this article, we discuss a data sharing and knowledge integration framework through autonomous agents with blockchain for implementing Electronic Health Records (EHR). This will enable us to augment existing blockchain-based EHR Systems. We discuss how major concerns in the health industry, i.e., trust, security and scalability, can be addressed by transitioning from existing models to convergence of the three technologies – blockchain, agent-based modeling, and knowledge graph in a decentralized ecosystem. Each autonomous agent is responsible for instantiating key processes, such as user authentication and authorization, smart contracts, and knowledge graph generation through data integration among the participating stakeholders in the network. We discuss a layered approach for the design of the proposed system leading to an enhanced, safer clinical decision-making system. This can pave the way toward more informed and engaged patients and citizens by delivering personalized healthcare.

Evidence-based Practice in Clinical Psychology

2014 ◽  
pp. 127-149
Author(s):  
Bonnie Spring ◽  
Kelly Neville
Keyword(s):  
Clinical Psychology ◽  
Health Professionals ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Clinical Decision ◽  
Evidence Based ◽  
Institute Of Medicine ◽  
Client Characteristics ◽  
Clinical Psychology Training ◽  
Century Health

The Institute of Medicine identifies evidence-based practice (EBP) as a core competence for all 21st century health professionals (Greiner & Knebel, 2003). Psychology is a relative newcomer to the evidence-based movement, having just adopted EBP as policy in 2005 (www2.apa.org/practice/ebpstatement.pdf). Evidence-based practice is both a conceptual model and a process for basing clinical decision-making on the integration of research, client characteristics, and resource considerations. We describe the evolution of models of EBP across the health disciplines and discuss how the concepts and methods of EBP apply in clinical psychology. Psychologists’ roles in relation to EBP are as creators, synthesizers, and consumers of evidence. We consider implications of EBP’s adoption for clinical psychology training, and describe learning resources that support clinical psychologists in mastering EBP.

scholarly journals “家庭共決模式”: 一種明智的臨床選擇

2013 ◽  
Vol 11 (2) ◽  
pp. 31-35
Author(s):  
Xinqing ZHANG
Keyword(s):  
Health Professionals ◽  
Clinical Decision Making ◽  
Healthcare Setting ◽  
Clinical Decision ◽  
Self Determination ◽  
Digital Commons ◽  
The Family ◽  
Key Factor ◽  
Family Member Involvement ◽  
Member Involvement

LANGUAGE NOTE | Document text in Chinese; abstract in English only.Professor Lo strongly argues that family co-determination, rather than self-determination or family-determination, is one of the best choices for protecting the vulnerable in a healthcare setting. The assumption underlying the family co-determination model is that of an individual as a person-in-the-family rather than as an isolated individual. I provide some proofs to enhance the Confucian bioethical base of family co-determination. Based on a national survey of health professionals and patients, I conclude that family member involvement in clinical decision making contributes to better communication between doctors and patients, which is a key factor in alleviating the degree of tension.DOWNLOAD HISTORY | This article has been downloaded 50 times in Digital Commons before migrating into this platform.

scholarly journals Health record systems that meet clinical needs

2012 ◽  
Author(s):  
Gabriella Negrini
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Patient Safety ◽  
Health Care Professionals ◽  
Clinical Decision Making ◽  
Clinical Information ◽  
Clinical Decision ◽  
Health Record ◽  
Record System ◽  
Health Records

Introduction Increased attention has recently been focused on health record systems as a result of accreditation programs, a growing emphasis on patient safety, and the increase in lawsuits involving allegations of malpractice. Health-care professionals frequently express dissatisfaction with the health record systems and complain that the data included are neither informative nor useful for clinical decision making. This article reviews the main objectives of a hospital health record system, with emphasis on its roles in communication and exchange among clinicians, patient safety, and continuity of care, and asks whether current systems have responded to the recent changes in the Italian health-care system.Discussion If health records are to meet the expectations of all health professionals, the overall information need must be carefully analyzed, a common data set must be created, and essential specialist contributions must be defined. Working with health-care professionals, the hospital management should define how clinical information is to be displayed and organized, identify a functionally optimal layout, define the characteristics of ongoing patient assessment in terms of who will be responsible for these activities and how often they will be performed. Internet technology can facilitate data retrieval and meet the general requirements of a paper-based health record system, but it must also ensure focus on clinical information, business continuity, integrity, security, and privacy.Conclusions The current health records system needs to be thoroughly revised to increase its accessibility, streamline the work of health-care professionals who consult it, and render it more useful for clinical decision making—a challenging task that will require the active involvement of the many professional classes involved.

scholarly journals Data-Driven Activities Involving Electronic Health Records: An Activity and Task Analysis Framework for Interactive Visualization Tools

2020 ◽  
Vol 4 (1) ◽  
pp. 7 ◽  
Author(s):  
Neda Rostamzadeh ◽  
Sheikh S. Abdullah ◽  
Kamran Sedig
Keyword(s):  
Electronic Health Records ◽  
Clinical Decision Making ◽  
Interactive Visualization ◽  
Clinical Decision ◽  
Data Driven ◽  
Reference List ◽  
Health Records ◽  
Visualization Tools ◽  
Electronic Health ◽  
Almost All

Electronic health records (EHRs) can be used to make critical decisions, to study the effects of treatments, and to detect hidden patterns in patient histories. In this paper, we present a framework to identify and analyze EHR-data-driven tasks and activities in the context of interactive visualization tools (IVTs)—that is, all the activities, sub-activities, tasks, and sub-tasks that are and can be supported by EHR-based IVTs. A systematic literature survey was conducted to collect the research papers that describe the design, implementation, and/or evaluation of EHR-based IVTs that support clinical decision-making. Databases included PubMed, the ACM Digital Library, the IEEE Library, and Google Scholar. These sources were supplemented by gray literature searching and reference list reviews. Of the 946 initially identified articles, the survey analyzes 19 IVTs described in 24 articles that met the final selection criteria. The survey includes an overview of the goal of each IVT, a brief description of its visualization, and an analysis of how sub-activities, tasks, and sub-tasks blend and combine to accomplish the tool’s main higher-level activities of interpreting, predicting, and monitoring. Our proposed framework shows the gaps in support of higher-level activities supported by existing IVTs. It appears that almost all existing IVTs focus on the activity of interpreting, while only a few of them support predicting and monitoring—this despite the importance of these activities in assisting users in finding patients that are at high risk and tracking patients’ status after treatment.

Medical Informatics and Clinical Decision Making: The Science and the Pragmatics

1991 ◽  
Vol 11 (4_suppl) ◽  
pp. S2-S14 ◽  
Author(s):  
Edward H. Shortliffe
Keyword(s):  
Decision Making ◽  
Medical Informatics ◽  
Ad Hoc ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Decision Makers ◽  
Medical Decision ◽  
Normative Models ◽  
Outcomes Management ◽  
Collaborative Efforts

There are important scientific and pragmatic synergies between the medical decision making field and the emerging discipline of medical informatics. In the 1970s, the field of medicine forced clinically oriented artificial intelligence (AI) researchers to develop ways to manage explicit statements of uncertainty in expert systems. Classic probability theory was considered and discussed, but it tended to be abandoned because of complexities that limited its use. In medical AI systems, uncertainty was handled by a variety of ad hoc models that simulated probabilistic considerations. To illustrate the scientific interactions between the fields, the author describes recent work in his laboratory that has attempted to show that formal normative models based on probability and decision theory can be practically melded with AI methods to deliver effective advisory tools. In addition, the practical needs of decision makers and health policy planners are increasingly necessitating collaborative efforts to develop a computing and communications infrastructure for the decision making and informatics communities. This point is illustrated with an example drawn from outcomes management research.

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