scholarly journals Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC)

2016 ◽  
Vol 23 (4) ◽  
pp. 796-801 ◽  
Author(s):  
James M Hoffman ◽  
Henry M Dunnenberger ◽  
J Kevin Hicks ◽  
Kelly E Caudle ◽  
Michelle Whirl Carrillo ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Point Of Care ◽  
Clinical Care ◽  
Clinical Information ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Knowledge Resources ◽  
Collective Experience ◽  
Implementation Resources ◽  
Machine Readable

Abstract To move beyond a select few genes/drugs, the successful adoption of pharmacogenomics into routine clinical care requires a curated and machine-readable database of pharmacogenomic knowledge suitable for use in an electronic health record (EHR) with clinical decision support (CDS). Recognizing that EHR vendors do not yet provide a standard set of CDS functions for pharmacogenetics, the Clinical Pharmacogenetics Implementation Consortium (CPIC) Informatics Working Group is developing and systematically incorporating a set of EHR-agnostic implementation resources into all CPIC guidelines. These resources illustrate how to integrate pharmacogenomic test results in clinical information systems with CDS to facilitate the use of patient genomic data at the point of care. Based on our collective experience creating existing CPIC resources and implementing pharmacogenomics at our practice sites, we outline principles to define the key features of future knowledge bases and discuss the importance of these knowledge resources for pharmacogenomics and ultimately precision medicine.

scholarly journals Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network

2021 ◽  
Vol 11 (7) ◽  
pp. 647
Author(s):  
Nina R. Sperber ◽  
Olivia M. Dong ◽  
Megan C. Roberts ◽  
Paul Dexter ◽  
Amanda R. Elsey ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Program Implementation ◽  
Clinical Care ◽  
Genomic Medicine ◽  
Implementation Strategies ◽  
Clinical Decision ◽  
Implementing Change ◽  
Implementation Outcomes ◽  
Planning Framework

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project’s strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common; however, operationalization of each differed in accordance with each study’s implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts.

Tools and Considerations to Develop the Blueprint for the Next Generation of Clinical Care Technology

2019 ◽  
Vol 8 (1) ◽  
pp. 1-8
Author(s):  
Chris Daniel Riha
Keyword(s):  
Information Systems ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Clinical Information ◽  
Clinical Decision ◽  
Clinical Engineering ◽  
Historical Background ◽  
Clinical Information Systems ◽  
Next Generation ◽  
Care Technology

This article provides a brief historical look at the genesis and evolution of clinical information systems. Based upon this historical background and the expertise of the authors, which encompasses, clinical, IT/cybersecurity, clinical engineering, as well as quality control expertise the article provides a roadmap for the next generation of clinical information systems. This next generation will not only provide consulting services to physicians via computer clinical decision support systems, but also the ability to perform autonomous and semi-autonomous care at the bedside via interfaces to medical devices (e.g. ventilators and infusion pumps), as well as auto ordering protocols.

An Overview of Efforts to Bring Clinical Knowledge to the Point of Care

2005 ◽  
pp. 285-296
Author(s):  
Dean F. Sittig
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Point Of Care ◽  
Clinical Decision Support Systems ◽  
Clinical Information ◽  
Clinical Decision ◽  
Clinical Knowledge ◽  
The Right

By bringing people the right information in the right format at the right time and place, state of the art clinical information systems with imbedded clinical knowledge can help people make the right clinical decisions. This chapter provides an overview of the efforts to develop systems capable of delivering such information at the point of care. The first section focuses on “library-type” applications that enable a clinician to look-up information in an electronic document. The second section describes a myriad of “real-time clinical decision support systems.” These systems generally deliver clinical guidance at the point of care within the clinical information system (CIS). The third section describes several “hybrid” systems, which combine aspects of real-time clinical decision support systems with library-type information. Finally, section four provides a brief look at various attempts to bring clinical knowledge, in the form of computable guidelines, to the point of care.be sufficiently expressive to explicitly capture the design rational (process and outcome intentions) of the guideline’s author, while leaving flexibility at application time to the attending physician and their own preferred methods.” (Shahar, 2001)

Information systems in support of public health in high-income countries

2021 ◽  
pp. 3-12
Author(s):  
Tjeerd-Pieter van Staa ◽  
Liam Smeeth
Keyword(s):  
Public Health ◽  
Information Systems ◽  
Disease Surveillance ◽  
Point Of Care ◽  
Clinical Care ◽  
Healthcare Providers ◽  
Clinical Information ◽  
Evidence Base ◽  
Healthcare Intervention ◽  
Healthcare Interventions

Public health activities are dependent on the availability of information and ability to disseminate information to clinicians/healthcare providers, individuals, and communities. The increasing computerization of healthcare systems can offer opportunities to improve these activities. Databases of electronic healthcare records are used for disease surveillance and monitoring healthcare interventions. The quality and quantity of reporting of notifiable diseases may be improved by regular review of the electronic healthcare records. Randomized trials that recruit patients at the point of care and use electronic healthcare records for collection of follow-up information can be used to test the effectiveness of healthcare intervention in routine clinical practice. Cluster trials that randomize different clinics or regions can compare different public health policies and improve the evidence base for the pragmatic use of public health interventions. Data generated within clinical information systems can be used to provide feedback and guidance to clinicians and patients as part of clinical care. Better information systems providing data on risks and benefits of healthcare interventions will provide an important impetus to evidence-based public health.

Effect of digital tumor board solutions on “failure-to-discuss” rates for patient cases during tumor boards.

2019 ◽  
Vol 37 (27_suppl) ◽  
pp. 308-308 ◽  
Author(s):  
Donna Fowler ◽  
Lincoln R Sheets ◽  
Matthew S Prime ◽  
Chaohui Guo ◽  
Athanasios Siadimas ◽  
...  
Keyword(s):  
Clinical Care ◽  
Interdisciplinary Approach ◽  
Clinical Information ◽  
Clinical Decision ◽  
Tumor Board ◽  
Tumor Boards ◽  
Rate Of Failure ◽  
Implementation Data ◽  
The Impact

308 Background: A multidisciplinary tumor board (MTB) provides an interdisciplinary approach for decision-making in cancer care. Efficient conduction of MTBs is importantfor optimal patient management. It is, however, often observed that prepared patient cases are not discussed during tumor boards due to limited time or incomplete information, which could cause delaytocaredecisions and/or the initiation of treatments. It remains unknown whether digital technologies canreduce the rate of failure to discuss during MTBs. Methods: A prospective cohort study was undertaken to evaluate the preparation & conduction of MTBs pre- & post-implementation of the NAVIFY Tumor Board (NTB) solution at Missouri University Health Care (MU), including the Ear, Nose & Throat (ENT) MTB. The NTB is a cloud-based workflow product, integrated with the hospital EMR, that aggregates and displays relevant clinical information. NTB was introduced to theMUENT MTB on Oct 10, 2018. Results: Pre-NTB implementation, data was collected from 42 ENT MTBs. A total of 551 patient cases were prepared for MTBs, but only 423 patient cases were discussed. This was an average “failure-to-discuss” rate of 19.4% per meeting (SD = 15.6%). After NTB implementation, data was collected from 7 MTBs where a total of 70 patient cases were prepared and discussed. There were no instances of failure to discuss, and as such, was significantlyreduced after the implementation of NTB (Mann-Whitney U test, p = 0.0004). The average number of patient cases discussed per meeting pre- and post-NTB implementation did not change (Mann-Whitney U test, p > 0.1) and meeting duration was the same. Conclusions: Introduction of the NTB did not change the weekly number of cases discussed, but did significantly reduce the failure to discuss rates for ENT MTB cases. Reducing failure to discuss rates could decrease the overall time to clinical decision and the initiation of treatment, which could potentially improve patient outcomes. Additional studies are needed to examine the impact of digital solutions on the quality of clinical care.

scholarly journals Standards for Scalable Clinical Decision Support: Need, Current and Emerging Standards, Gaps, and Proposal for Progress

2010 ◽  
Vol 4 (1) ◽  
pp. 235-244 ◽  
Author(s):  
Kensaku Kawamoto ◽  
Guilherme Del Fiol ◽  
David F. Lobach ◽  
Robert A Jenders
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Ad Hoc ◽  
Cost Effective ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Knowledge Resources ◽  
Limited Availability ◽  
Information Models

Despite their potential to significantly improve health care, advanced clinical decision support (CDS) capabilities are not widely available in the clinical setting. An important reason for this limited availability of CDS capabilities is the application-specific and institution-specific nature of most current CDS implementations. Thus, a critical need for enabling CDS capabilities on a much larger scale is the development and adoption of standards that enable current and emerging CDS resources to be more effectively leveraged across multiple applications and care settings. Standards required for such effective scaling of CDS include (i) standard terminologies and information models to represent and communicate about health care data; (ii) standard approaches to representing clinical knowledge in both human-readable and machine-executable formats; and (iii) standard approaches for leveraging these knowledge resources to provide CDS capabilities across various applications and care settings. A number of standards do exist or are under development to meet these needs. However, many gaps and challenges remain, including the excessive complexity of many standards; the limited availability of easily accessible knowledge resources implemented using standard approaches; and the lack of tooling and other practical resources to enable the efficient adoption of existing standards. Thus, the future development and widespread adoption of current CDS standards will depend critically on the availability of tooling, knowledge bases, and other resources that make the adoption of CDS standards not only the right approach to take, but the cost-effective path to follow given the alternative of using a traditional, ad hoc approach to implementing CDS.

Bringing the best evidence to the point of care

2010 ◽  
pp. 22-27
Author(s):  
Paul P. Glasziou
Keyword(s):  
Patient Care ◽  
Evidence Based Medicine ◽  
Research Evidence ◽  
Point Of Care ◽  
Clinical Care ◽  
Clinical Decision ◽  
Evidence Based ◽  
Based Medicine ◽  
Quality Of Research

You must always be students, learning and unlearning till your life’s end. Joseph Lister Neither our memories nor our textbooks are complete and up to date with all the research relevant to the patients we will see today. The scattering of necessary research across a vast ocean of literature makes it inaccessible at the point of clinical decision. The consequences for patient care have given rise to the discipline of evidence-based medicine (EBM), whose two central concerns are with the quality of research evidence and with its appropriate usage in clinical care....

scholarly journals Applying Precision Medicine in Clinical Practice

2020 ◽  
Vol 95 (6) ◽  
pp. 382-386
Author(s):  
Kyu-pyo Kim
Keyword(s):  
Clinical Practice ◽  
Precision Medicine ◽  
Clinical Care ◽  
Clinical Information ◽  
Data Systems ◽  
Data Collection And Analysis ◽  
Clinical Records ◽  
Next Generation Sequencing Ngs ◽  
Modern Era ◽  
Generation Sequencing

Precision medicine is the modern era version of “personalized medicine”, which integrates data from genomics and clinical information to optimize the care delivered to patients. As next generation sequencing (NGS) revolutionized the speed and cost of genomic sequencing, precision medicine entered clinical practice in 2017 via the national reimbursement of oncology and rare diseases. In parallel, the digitalization of clinical data through electronic health recording (EHR) and hospital information systems has allowed data collection and analysis. This has led to the integration of biomarkers and clinical records, which have introduced precision medicine into clinical practice. Today, many countries and medical institutes are endeavoring to create systems that will enable precision medicine to be applied to clinical practice. These data systems will benefit the patient by providing accurate data based on his/her characteristics rather than the conventional approach of using “average data”. Internal medicine will transform into a data-driven science that enables physicians to translate molecular biomarkers and big data analysis into improved clinical care.

An Overview of Efforts to Bring Clinical Knowledge to the Point of Care

2011 ◽  
pp. 222-231
Author(s):  
Dean F. Sittig
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Point Of Care ◽  
Clinical Decision Support Systems ◽  
Clinical Information ◽  
Clinical Decision ◽  
Clinical Knowledge ◽  
The Right

By bringing people the right information in the right format at the right time and place, state of the art clinical information systems with imbedded clinical knowledge can help people make the right clinical decisions. This chapter provides an overview of the efforts to develop systems capable of delivering such information at the point of care. The first section focuses on “library-type” applications that enable a clinician to look-up information in an electronic document. The second section describes a myriad of “realtime clinical decision support systems.” These systems generally deliver clinical guidance at the point of care within the clinical information system (CIS). The third section describes several “hybrid” systems, which combine aspects of real-time clinical decision support systems with library-type information. Finally, section four provides a brief look at various attempts to bring clinical knowledge, in the form of computable guidelines, to the point of care.

scholarly journals What's the Score? Do Pleural Effusion Clinical Scoring Systems Help in Management of Disease?

2019 ◽  
Vol 40 (03) ◽  
pp. 394-401
Author(s):  
Peter Grendelmeier ◽  
Najib M. Rahman
Keyword(s):  
Pleural Effusion ◽  
Clinical Care ◽  
Length Of Hospital Stay ◽  
Clinical Information ◽  
Scoring Systems ◽  
Clinical Decision ◽  
Good Prognosis ◽  
Prognostic Models ◽  
Routine Practice ◽  
Pleural Infection

AbstractPleural effusion is a common condition, affecting over 3,000 people per million population every year. More than 50 causes of pleural effusions are known, including pleural infection and malignant pleural disease. These conditions place a large burden on healthcare systems with one-fourth of patients with pleural infection having a length of hospital stay of more than 1 month. Malignant pleural effusion represents advanced malignant disease with a correspondingly high mortality. Prognostic models using clinical information in combination with blood or pleural fluid biomarkers predicting survival and other outcome measures are therefore a priority in improving clinical care, and potentially outcomes. Identifying patients with poor prognosis may help avoid discomfort and unnecessary interventions at the end of their lives, while, on the other hand, individuals with scores predicting a particularly good prognosis might be selected for more aggressive early treatment. Such scores must be based on data representing routine practice in a general hospital and variables chosen based on their clinical availability at clinical decision points (i.e., before treatment is instituted), making the findings widely applicable.

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