scholarly journals A best-fit solution: transforming an NHS Library and Knowledge Service in readiness for a new hospital building without a traditional library space

2021 ◽  
Vol 109 (3) ◽  
Author(s):  
Rebecca Jean Scott
Keyword(s):  
Health Care ◽  
Clinical Setting ◽  
Health Care Professionals ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Development Framework ◽  
Literature Searches ◽  
The Hill ◽  
Best Fit ◽  
Knowledge Services

Background: Over a decade ago, the Hill report argued that a shift in vision was required to change the perception of National Health Service (NHS) Library and Knowledge Services (LKS) in England from “book repositories” to essential services that underpin clinical decision-making by patients, carers, and health care professionals. Health Education England’s Knowledge for Healthcare: A Development Framework for Library and Knowledge Services in England 2015–2020 advocates embedding librarians within clinical and management teams in order to provide access to high-quality evidence at the point of need.Case presentation: In April 2019, Royal Papworth Hospital relocated twelve miles from its historic village location in Papworth Everard to its new state-of-the-art hospital on the Cambridge Biomedical Campus. The design for this new hospital did not accommodate a traditional library space and therefore necessitated a transformation of the LKS. The organization opted to embed the LKS staff into the clinical setting and relegate 80% of the print collection to off-site storage. This project and its associated steps are presented as an example of health care library transformation.Conclusion: Embedding the LKS team in the clinical setting, engaging in proactive outreach activity, and improving our marketing led to a 44% increase in literature searches requested compared to the same eleven-month period in the previous year. A 40% decrease in our print book loans indicates additional barriers to using a click-and-collect service and the need for greater investment in our e-book provision. However, early outcomes for our best-fit service transformation are positive. Having an open, dual mindset has enabled the service to embrace change and maximize emerging opportunities to collaborate with clinical staff on new projects.

scholarly journals Development of a stepwise tool to aide primary health care professionals in the process of deprescribing in older persons

2020 ◽  
Vol 18 (4) ◽  
pp. 2033
Author(s):  
Roxana De las Salas ◽  
Javier Eslava-Schmalbach ◽  
Claudia Vaca-González ◽  
Dolores Rodríguez ◽  
Albert Figueras
Keyword(s):  
Health Care ◽  
Primary Health Care ◽  
Health Care Professionals ◽  
Older Persons ◽  
Clinical Decision Making ◽  
Inappropriate Medication ◽  
Clinical Decision ◽  
Potentially Inappropriate Medication ◽  
Kappa Statistics ◽  
Primary Health

Objective: The aim of this study was to develop and validate a stepwise tool to aid primary health care professionals in the process of deprescribing potentially inappropriate medication in older persons. Methods: We carried out a systematic review to identify previously published tools. A composite proposal of algorithm was made by following the steps from clinical experience to deprescribe medications. A 2-round electronic Delphi method was conducted to establish consensus. Eighteen experts from different countries (Colombia, Spain and Argentina) accepted to be part of the panel representing geriatricians, internists, endocrinologist, general practitioners, pharmacologists, clinical pharmacists, family physicians and nurses. Panel members were asked to mark a Likert Scale from 1 to 9 points (1= strongly disagree, 9= strongly agree). The content validity‏ ratio, item-level content validity, and Fleiss’ Kappa statistics was measured to establish reliability. The same voting method was used for round 2. Results: A 7-question algorithm was proposed. Each question was part of a domain and conduct into a decision. In round 1, a consensus was not reached but statements were grouped and organized. In round 2, the tool met consensus. The inter-rater reliability was between substantial and almost perfect for questions with Kappa=0.77 (95% CI 0.60-0.93), for domains with Kappa= 0.73 (95%CI 0.60-0.86) and for decisions with Kappa= 0.97 (95%CI 0.90-1.00). Conclusions: This is a novel tool that captures and supports healthcare professionals in clinical decision-making for deprescribing potentially inappropriate medication. This includes patient’s and caregiver’s preferences about medication. This tool will help to standardize care and provide guidance on the prescribing/deprescribing process of older persons’ medications. Also, it provides a holistic way to reduce polypharmacy and inappropriate medications in clinical practice.

scholarly journals Clinical Decision-Making Tool for Safe and Effective Prescription of Exercise in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Results From an Interdisciplinary Delphi Survey and Focus Groups

2015 ◽  
Vol 95 (10) ◽  
pp. 1387-1396 ◽  
Author(s):  
Pat G. Camp ◽  
W. Darlene Reid ◽  
Frank Chung ◽  
Ashley Kirkham ◽  
Dina Brooks ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Health Care ◽  
Focus Groups ◽  
Pulmonary Disease ◽  
Health Care Professionals ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Chronic Obstructive ◽  
Delphi Panel ◽  
Obstructive Pulmonary Disease

Background Exercise is recommended for people with an acute exacerbation of chronic obstructive pulmonary disease (AECOPD), yet there is little information to guide safe and effective mobilization and exercise for these patients. Objectives The purpose of this study was to develop a clinical decision-making tool to guide health care professionals in the assessment, prescription, monitoring, and progression of mobilization and therapeutic exercise for patients with AECOPD. Design and Methods A 3-round interdisciplinary Delphi panel identified and selected items based on a preselected consensus of 80%. These items were summarized in a paper-based tool titled Mobilization in Acute Exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD-Mob). Focus groups and questionnaires were subsequently used to conduct a sensibility evaluation of the tool. Results Nine researchers, 13 clinicians, and 7 individuals with COPD identified and approved 110 parameters for safe and effective exercise in AECOPD. These parameters were grouped into 5 categories: (1) “What to Assess Prior to Mobilization,” (2) “When to Consider Not Mobilizing or to Discontinue Mobilization,” (3) “What to Monitor During Mobilization for Patient Safety,” (4) “How to Progress Mobilization to Enhance Effectiveness,” and (5) “What to Confirm Prior to Discharge.” The tool was evaluated in 4 focus groups of 18 health care professionals, 90% of whom reported the tool was easy to use, was concise, and would guide a health care professional who is new to the acute care setting and working with patients with AECOPD. Limitations The tool was developed based on published evidence and expert opinion, so the applicability of the items to patients in all settings cannot be guaranteed. The Delphi panel consisted of health care professionals from Canada, so items may not be generalizable to other jurisdictions. Conclusions The AECOPD-Mob provides practical and concise information on safe and effective exercise for the AECOPD population for use by the new graduate or novice acute care practitioner.

scholarly journals Introducing Artificial Intelligence Training in Medical Education

10.2196/16048 ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. e16048 ◽  
Author(s):  
Ketan Paranjape ◽  
Michiel Schinkel ◽  
Rishi Nannan Panday ◽  
Josip Car ◽  
Prabath Nanayakkara
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Medical Education ◽  
Health Care Professionals ◽  
Access To Health Care ◽  
Clinical Decision Making ◽  
New Technology ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Use Of Data

Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.

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 Introducing Artificial Intelligence Training in Medical Education (Preprint)

2019 ◽  
Author(s):  
Ketan Paranjape ◽  
Michiel Schinkel ◽  
Rishi Nannan Panday ◽  
Josip Car ◽  
Prabath Nanayakkara
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Medical Education ◽  
Health Care Professionals ◽  
Access To Health Care ◽  
Clinical Decision Making ◽  
New Technology ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Use Of Data

UNSTRUCTURED Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.

scholarly journals Real time location systems: Is it big brother or a big opportunity for professional practice?

2020 ◽  
Vol 10 (11) ◽  
pp. 67
Author(s):  
Jacqueline Limoges ◽  
Sara Lankshear ◽  
Joseph Church
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Real Time ◽  
Professional Practice ◽  
Health Professionals ◽  
Health Care Professionals ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Care Providers ◽  
Location Systems

Purpose: Real Time Location Systems (RTLS) is an emerging health care technology with the potential to capture data that can be used to improve professional practice and patient outcomes. However, there is a paucity of literature in this area to guide health professionals and leaders in both the implementation and use of RTLS data. To address this gap in the literature, this qualitative study was designed to explore how staff perceive and experience RTLS, and how health care providers anticipate using RTLS data for professional practice and clinical decision making.Results: Interviews and focus groups were conducted with 31 health care professionals who work in a community hospital in Canada. There was variation between the participants in terms of the experience of being monitored, the intensity of emotions related to RTLS and being monitored, the degree to which RTLS influenced clinical decision making and reflection, and the perceptions of usefulness of RTLS data for  professional practice. Three key themes emerged from the data: (1) the experience of being monitored, (2) anticipating using the data and (3) claiming the data for professional practice.Conclusions: Supports are vital to the successful adoption of RTLS and to enable health care professionals to claim and use RTLS data for professional practice and clinical decision making. During the implementation and use of RTLS data, it is crucial to recognize that RTLS data only represent the time spent in a location, and not the professional or knowledge-based practice of health professionals. Further research is required to understand the leadership strategies to guide the use of RTLS data.

What Is Medical Ethics Consultation?

2008 ◽  
Vol 36 (1) ◽  
pp. 95-118 ◽  
Author(s):  
Giles R. Scofield
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Medical Ethics ◽  
Medical Technology ◽  
Clinical Decision Making ◽  
Ethics Consultation ◽  
Clinical Decision ◽  
Law Students ◽  
Medical Ethicist ◽  
Care Decision

As everybody knows, advances in medicine and medical technology have brought enormous benefits to, and created vexing choices for, us all – choices that can, and occasionally do, test the very limits of thinking itself. As everyone also knows, we live in the age of consultants, i.e., of professional experts who are ready, willing, and able to give us advice on any and every conceivable question. One such consultant is the medical ethics consultant, or the medical ethicist who consults.Medical ethics consultants involve themselves in just about every aspect of health care decision making. They help legislators and judges determine law, hospitals formulate policies, medical schools develop curricula, etc. In addition to educating physicians, nurses, and lawyers, amongst others, including medical, nursing, and law students, they participate in clinical decision making at the bedside.

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