Relevant bibliographies by topics / AI health care planning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI health care planning'

Author: Grafiati
Published: 29 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "AI health care planning"

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Nelson, Scott D., Colin G. Walsh, Casey A. Olsen, Andrew J. McLaughlin, Joseph R. LeGrand, Nick Schutz, and Thomas A. Lasko. "Demystifying artificial intelligence in pharmacy." American Journal of Health-System Pharmacy 77, no. 19 (July 4, 2020): 1556–70. http://dx.doi.org/10.1093/ajhp/zxaa218.

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Abstract Purpose To provide pharmacists and other clinicians with a basic understanding of the underlying principles and practical applications of artificial intelligence (AI) in the medication-use process. Summary “Artificial intelligence” is a general term used to describe the theory and development of computer systems to perform tasks that normally would require human cognition, such as perception, language understanding, reasoning, learning, planning, and problem solving. Following the fundamental theorem of informatics, a better term for AI would be “augmented intelligence,” or leveraging the strengths of computers and the strengths of clinicians together to obtain improved outcomes for patients. Understanding the vocabulary of and methods used in AI will help clinicians productively communicate with data scientists to collaborate on developing models that augment patient care. This primer includes discussion of approaches to identifying problems in practice that could benefit from application of AI and those that would not, as well as methods of training, validating, implementing, evaluating, and maintaining AI models. Some key limitations of AI related to the medication-use process are also discussed. Conclusion As medication-use domain experts, pharmacists play a key role in developing and evaluating AI in healthcare. An understanding of the core concepts of AI is necessary to engage in collaboration with data scientists and critically evaluating its place in patient care, especially as clinical practice continues to evolve and develop.
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Schwendicke, F., W. Samek, and J. Krois. "Artificial Intelligence in Dentistry: Chances and Challenges." Journal of Dental Research 99, no. 7 (April 21, 2020): 769–74. http://dx.doi.org/10.1177/0022034520915714.

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The term “artificial intelligence” (AI) refers to the idea of machines being capable of performing human tasks. A subdomain of AI is machine learning (ML), which “learns” intrinsic statistical patterns in data to eventually cast predictions on unseen data. Deep learning is a ML technique using multi-layer mathematical operations for learning and inferring on complex data like imagery. This succinct narrative review describes the application, limitations and possible future of AI-based dental diagnostics, treatment planning, and conduct, for example, image analysis, prediction making, record keeping, as well as dental research and discovery. AI-based applications will streamline care, relieving the dental workforce from laborious routine tasks, increasing health at lower costs for a broader population, and eventually facilitate personalized, predictive, preventive, and participatory dentistry. However, AI solutions have not by large entered routine dental practice, mainly due to 1) limited data availability, accessibility, structure, and comprehensiveness, 2) lacking methodological rigor and standards in their development, 3) and practical questions around the value and usefulness of these solutions, but also ethics and responsibility. Any AI application in dentistry should demonstrate tangible value by, for example, improving access to and quality of care, increasing efficiency and safety of services, empowering and enabling patients, supporting medical research, or increasing sustainability. Individual privacy, rights, and autonomy need to be put front and center; a shift from centralized to distributed/federated learning may address this while improving scalability and robustness. Lastly, trustworthiness into, and generalizability of, dental AI solutions need to be guaranteed; the implementation of continuous human oversight and standards grounded in evidence-based dentistry should be expected. Methods to visualize, interpret, and explain the logic behind AI solutions will contribute (“explainable AI”). Dental education will need to accompany the introduction of clinical AI solutions by fostering digital literacy in the future dental workforce.
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Fieldhouse, Jon, Vanessa Parmenter, and Alice Hortop. "Vocational rehabilitation in mental health services: evaluating the work of a social and therapeutic horticulture community interest company." Mental Health and Social Inclusion 18, no. 3 (August 5, 2014): 155–63. http://dx.doi.org/10.1108/mhsi-01-2014-0002.

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Purpose – The purpose of this paper is to report on an action inquiry (AI) evaluation of the Natureways project, a time-limited collaboration between an NHS Trust Vocational Service and a voluntary sector horticulture-based community interest company (CIC). Design/methodology/approach – Natureways produced positive employment outcomes and an AI process – based on co-operative inquiry with trainees, staff, and managers – explored how these had been achieved. Findings – Natureways’ efficacy was based on features of the setting (its supportiveness, rural location, and workplace authenticity), on its embeddedness (within local care-planning pathways, the horticultural industry, and the local community), and on effective intersectoral working. The inquiry also generated actionable learning about creative leadership and adaptability in the changing landscape of service provision, about the benefits of the CIC's small scale and business ethos, about the links between trainees’ employability, social inclusion and recovery, about horticulture as a training medium, and about the role of AI in service development. Practical implications – The inquiry highlights how an intersectoral CIC can be an effective model for vocational rehabilitation. Social implications – Community-embeddeness is an asset for mental health-orientated CICs, facilitating social inclusion and recovery. Social and therapeutic horticulture settings are seen to be conducive to this. Originality/value – This case study suggests that AI methodology is not only well-suited to many practitioners’ skill sets, but its participatory ethos and focus on experiential knowledge makes it suitable for bringing a service user voice to bear on service development.
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Calders, Toon, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, and Salvatore Ruggieri. "Introduction to The Special Section on Bias and Fairness in AI." ACM SIGKDD Explorations Newsletter 23, no. 1 (May 26, 2021): 1–3. http://dx.doi.org/10.1145/3468507.3468509.

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Fairness in Artificial Intelligence rightfully receives a lot of attention these days. Many life-impacting decisions are being partially automated, including health-care resource planning decisions, insurance and credit risk predictions, recidivism predictions, etc. Much of work appearing on this topic within the Data Mining, Machine Learning and Artificial Intelligence community is focused on technological aspects. Nevertheless, fairness is much wider than this as it lies at the intersection of philosophy, ethics, legislation, and practical perspectives. Therefore, to fill this gap and bring together scholars of these disciplines working on fairness, the first workshop on Bias and Fairness in AI was held online on September 18, 2020 at the ECML-PKDD 2020 conference. This special section includes six articles presenting different perspectives on bias and fairness from different angles.
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Blanes-Selva, Vicent, Ascensión Doñate-Martínez, Gordon Linklater, Jorge Garcés-Ferrer, and Juan M. García-Gómez. "Responsive and Minimalist App Based on Explainable AI to Assess Palliative Care Needs during Bedside Consultations on Older Patients." Sustainability 13, no. 17 (September 2, 2021): 9844. http://dx.doi.org/10.3390/su13179844.

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Palliative care is an alternative to standard care for gravely ill patients that has demonstrated many clinical benefits in cost-effective interventions. It is expected to grow in demand soon, so it is necessary to detect those patients who may benefit from these programs using a personalised objective criterion at the correct time. Our goal was to develop a responsive and minimalist web application embedding a 1-year mortality explainable predictive model to assess palliative care at bedside consultation. A 1-year mortality predictive model has been trained. We ranked the input variables and evaluated models with an increasing number of variables. We selected the model with the seven most relevant variables. Finally, we created a responsive, minimalist and explainable app to support bedside decision making for older palliative care. The selected variables are age, medication, Charlson, Barthel, urea, RDW-SD and metastatic tumour. The predictive model achieved an AUC ROC of 0.83 [CI: 0.82, 0.84]. A Shapley value graph was used for explainability. The app allows identifying patients in need of palliative care using the bad prognosis criterion, which can be a useful, easy and quick tool to support healthcare professionals in obtaining a fast recommendation in order to allocate health resources efficiently.
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Comiford, Ashley L., Dorothy A. Rhoades, Justin D. Dvorak, Kai Ding, Toral Mehta, Paul Spicer, Theodore Wagener, and Mark P. Doescher. "Use of Potentially Reduced Exposure Tobacco Products Among American Indian Smokeless Tobacco Users: Associations With Cessation Behaviors and Cotinine Levels." Public Health Reports 135, no. 1 (December 13, 2019): 141–49. http://dx.doi.org/10.1177/0033354919893031.

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Objectives: American Indian/Alaska Native (AI/AN) adults use smokeless tobacco products (eg, chewing and dip tobacco) more often than other racial/ethnic groups do. Although US adults increasingly use potentially reduced exposure tobacco products (PREPs), such as electronic cigarettes and snus, no studies have examined the use of PREPs among AI/AN smokeless tobacco users. We examined associations between current PREPs use and smokeless tobacco–related measures, including cessation attempts and cotinine levels, in a sample of American Indian adults who currently use smokeless tobacco. Methods: We collected survey and tobacco biomarker data from 299 adult American Indian smokeless tobacco users at Cherokee Nation health care facilities and events in 2016 and 2017. We used multivariable analyses to determine associations between current PREPs use and smokeless tobacco–related characteristics. Results: Current PREPs users were younger, less likely to be married or living with a partner, less likely to report a chronic medical condition, and more likely to report other tobacco use than PREPs nonusers. Among participants with annual household incomes ≤$30 000, current PREPs users were less likely than PREPs nonusers to report a definite desire to quit smokeless tobacco ( P = .02). PREPs use was not associated with planning to quit smokeless tobacco, past 12-month smokeless tobacco quit attempts, amount of smokeless tobacco used per week, cotinine levels, or scores on the Fagerström Test for Nicotine Dependence–Smokeless Tobacco. Conclusions: Our study suggests that American Indian smokeless tobacco users may not be using PREPs as a smokeless tobacco cessation aid. Future studies should take this finding into consideration when evaluating the role of PREPs use in smokeless tobacco cessation and in total tobacco cessation in this population.
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Adikari, Achini, Rashmika Nawaratne, Daswin De Silva, Sajani Ranasinghe, Oshadi Alahakoon, and Damminda Alahakoon. "Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence." Journal of Medical Internet Research 23, no. 4 (April 30, 2021): e27341. http://dx.doi.org/10.2196/27341.

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Background The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic. Objective This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework. Methods Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. Results The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens’ mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P<.05) in the second lockdown, showing increased frustration. Temporal emotion analysis was conducted by modeling the emotion state changes across the four stages of the pandemic, which demonstrated how different emotions emerged and shifted over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles. Conclusions This study showed that the diverse emotions and concerns that were expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study established the use of social media to discover informed insights during a time when physical communication was impossible, the outcomes could also contribute toward postpandemic recovery and understanding psychological impact via emotion changes, and they could potentially inform health care decision making. This study exploited AI and social media to enhance our understanding of human behaviors in global emergencies, which could lead to improved planning and policy making for future crises.
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Vitianingsih, Anik Vega, Achmad Choiron, Dwi Cahyono, and Suyanto Suyanto. "Representation of Spatial Data Modeling Results Measles Diseases, Case Study in East Java Province." Record and Library Journal 6, no. 1 (April 13, 2020): 14. http://dx.doi.org/10.20473/rlj.v6-i1.2020.14-23.

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Background of the study: Measles is a major cause of child death caused by a lack of immunization when a child is a baby.Purpose: The discussion in this paper aims to describe the results of the analysis of the spatial data modeling of measles, knowing the percentage distribution of measles-prone areas, each district based on coverage on immunization status with good, average, fair and poor classification categories. The classification results include areas with good, average, fair, and poor immunization coverage status categories.Method: The method used i.e. with to requirement gathering information data from the East Java health profile book in 2011-2016 for the measles attribute, a literature study to describe the parameter requirements based on the coverage of immunization status (infant immunization status, PD3I, epidemic, and nutritional status), and selection of artificial intelligent (AI) system methods that are in accordance with data behavior for the spatial data modeling process in the formulation of alternative preference values with a decision-making system that involves multi-criteria parameters (multiple attributes decision-making/MADM) with Simple Additive Weighting (SAW) method.Findings: The alternative preference value Vi in the spatial data modeling process with the SAW method can be used as a mathematical model for the same data series behavior.Conclusion: The results of the representation in the modeling of spatial data and this attribute data can be used as a reference for planning in the development of health care centers in areas with poor immunization status categories.
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Kerr, Bradley, David Stephens, Daniel Pham, Thomas Ghost Dog, Celena McCray, Colbie Caughlan, Amanda Gaston, et al. "Assessing the Usability, Appeal, and Impact of a Web-Based Training for Adults Responding to Concerning Posts on Social Media: Pilot Suicide Prevention Study." JMIR Mental Health 7, no. 1 (January 20, 2020): e14949. http://dx.doi.org/10.2196/14949.

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Background Suicide prevention remains challenging among youth, as many do not disclose suicidal ideation. Nearly one-third of American Indian and Alaska Native (AI and AN, tribal, or native) youth see concerning messages on social media at least weekly. Objective To prepare adults to support AI and AN youth who post or view concerning messages, our team designed an hour-long training: Responding to Concerning Posts on Social Media. This study tested the usability, appeal, and impact of the training. Methods A purposive sample of 70 adults was recruited to participate in the pilot, which included 2 study arms. Arm 1 participants completed a 30-min training video and reviewed accompanying handouts, including the Viewer Care Plan (VCP). The VCP provided a 3-step planning and response tool: (1) Start the Conversation, (2) Listen, Gather Information, and Assess Viewer Experience, and (3) Plan and Act. The intent of the VCP was to support and connect AI and AN youth who either view or post concerning messages on social media to life-saving resources. Those enrolled in arm 2 participated in an additional interactive role-play scenario with a coach that took place after the training, via text message. Participants provided qualitative and quantitative feedback on the training’s relevance, appeal, and utility. Paired t tests were used to assess confidence in addressing concerning posts between pre- and postsurveys. Content analysis of the role-play transcripts was used to assess the quality and completion of the coached role-plays, in relation to the recommended VCP. Results Altogether, 35 participants finished the training and completed pre- and postsurveys; 22 participants completed the 6-month follow-up survey. Pre-post analyses of differences in means found significant improvement across several efficacy measures, including confidence starting a conversation about social media (P=.003), confidence contacting the person who posted something concerning (P<.001), and confidence recommending support services to youth who view (P=.001) or youth who post concerning messages (P<.001). Similarly, pre- to 6-month analyses found significant positive improvement across multiple measures, including confidence contacting the youth who posted (P<.001), confidence starting a conversation about social media with youth (P=.003), and an increase in the number of experiences recommending resources for youth who viewed concerning social media posts (P=.02). Of the 3 steps of the VCP, the least followed step in coached role-plays was sharing tools and resources, which is a part of the third Plan and Act step. Conclusions Findings indicate that the Responding to Concerning Posts on Social Media training is a promising tool to prepare adults to intervene and complete the VCP. Additional evaluation with a larger cohort of participants is needed to determine the unique impact of the role-play scenario and changes in mental health referral rates, behaviors, and skills.
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Sumit. "AI Health Care Chatbot." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 13, 2020): 219–24. http://dx.doi.org/10.46501/ijmtst061241.

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Healthcare bot is a technology that makes interaction between man and machine possible by using Artificial Intelligence with the support of dialog flow. Now a day people tend to seek knowledge or information from internet that concern with health through online healthcare services. To lead a good life healthcare is very much important. But it is very difficult to obtain the consultation with the doctor in case of any health issues. The basic aim of this system is to bridge the vocabulary gap between the doctors by giving self-diagnosis from the comfort of one’s place. The proposed idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. To reduce the healthcare costs and improve accessibility to medical knowledge the medical bot is built. Certain bots act as a medical reference books, which helps the patient know more about their disease and helps to improve their health. The user can achieve the real benefit of a bot only when it can diagnose all kind of disease and provide necessary information. Hence, people will have an idea about their health and have the right protection.
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Dissertations / Theses on the topic "AI health care planning"

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Berggren, Andreas, Martin Gunnarsson, and Johannes Wallin. "Artificial intelligence as a decision support system in property development and facility management." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-25535.

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The construction industry has been hesitant for a long time to apply new technologies. In property development, the industry relies heavily on employees bringing experience from one project to another. These employees learn to manage risks in connection with the acquisition of land, but when these people retire, the knowledge disappears. An AI-based decision-support system that takes the risks and the market into account when acquiring land can learn from each project and bring this knowledge into future projects. In facility management, artificial intelligence could increase the efficiency of the allocation of staff in the ongoing operations. The purpose of the study is to analyse how companies in the real estate industry can improve their decision-making with the help of AI in property development and property management. In this study, two case studies of two different players in the real estate industry have been performed. One player, Bygg-Fast, represents property development and the other player, VGR, represents facility management. The study is based on interviews, discussions, and collected data. By mapping and then quantifying the risks and market indicators that are input data in the process, a basis can be created. The data can be used for a model that lays the foundation for an AI-based decision support system that will help the property developer to make calculated decisions in the land acquisition process. By mapping what a flow through a property looks like, measuring points can be set out to analyse how long the activities take in the specific business. These measured values provide a collection of data that makes it easier to plan the activities conducted in the property. A more efficient flow can be achieved by visualizing the entire process so staff can be allocated to the right part of the flow. By being flexible and being able to re-plan the business quickly if planning is disrupted, a high level of efficiency can be achieved. This could be done by an AI-based decision support system that simulates alternative day plans.
Byggbranschen har länge varit tveksamt till att applicera nya tekniker. Inom fastighetsutveckling bygger branschen mycket på att anställda tar med sig erfarenheter från ett projekt till ett annat. Dessa anställda lär sig hantera risker i samband med förvärv av mark men när dessa personer slutar eller går i pension försvinner kunskapen. Ett AI baserat beslutssystem som tar risk och marknad i beaktning vid förvärv av mark kan lära sig av varje projekt och ta med dessa kunskaper till framtida projekt. Inom fastighetsförvaltning skulle artificiell intelligens kunna effektivisera allokerandet av personal i den pågående verksamheten. Syftet med studien är att analysera hur företag i fastighetsbranschen kan förbättra sitt beslutstagande med hjälp av AI i utveckling av fastigheter samt fastighetsförvaltning. I denna studien har två fallstudier av två olika aktörer i fastighetsbranschen utförts. Ena aktören, Bygg-Fast, representerar fastighetsutveckling och den andra aktören, VGR, representerar fastighetsförvaltning. Studien bygger på intervjuer, diskussioner och insamlade data. Genom att kartlägga och sedan kvantifiera de risker samt marknadsindikatorer som är indata i processen kan ett underlag skapas. Underlaget kan användas för en modell som lägger grunden för ett AI baserat beslutsstödsystem som ska hjälpa fastighetsutvecklaren med att ta kalkylerade beslut i mark förvärvsprocessen. Genom att kartlägga hur ett flöde genom en fastighet ser ut kan mätpunkter sättas ut för att analysera hur lång tid aktiviteterna tar i den specifika verksamheten. Dessa mätvärden ger en samlad data som gör det lättare att planera verksamheten som bedrivs i fastigheten. Ett effektivare flöde kan uppnås genom att visualisera hela processen så personal kan allokeras till rätt del av flödet. Genom att vara flexibel och kunna planera om verksamheten snabbt ifall planering störs kan en hög effektivitet nås. Detta skulle kunna göras av ett AI baserat beslutsstödsystem som simulerar alternativa dagsplaneringar.
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Bennett, Ashlea R. "Home health care logistics planning." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33989.

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This thesis develops quantitative methods which incorporate transportation modeling for tactical and operational home health logistics planning problems. We define home health nurse routing and scheduling (HHNRS) problems, which are dynamic periodic routing and scheduling problems with fixed appointment times, where a set of patients must be visited by a home health nurse according to a prescribed weekly frequency for a prescribed number of consecutive weeks during a planning horizon, and each patient visit must be assigned an appointment time belonging to an allowable menu of equally-spaced times. Patient requests are revealed incrementally, and appointment time selections must be made without knowledge of future requests. First, a static problem variant is studied to understand the impact of fixed appointment times on routing and scheduling decisions, independent of other complicating factors in the HHNRS problem. The costs of offering fixed appointment times are quantified, and purely distance-based heuristics are shown to have potential limitations for appointment time problems unless proposed arc cost transformations are used. Building on this result, a new rolling horizon capacity-based heuristic is developed for HHNRS problems. The heuristic considers interactions between travel times, service times, and the fixed appointment time menu when inserting appointments for currently revealed patient requests into partial nurse schedules. The heuristic is shown to outperform a distance-based heuristic on metrics which emphasize meeting as much patient demand as possible. The home health nurse districting (HHND) problem is a tactical planning problem which influences HHNRS problem solution quality. A set of geographic zones must be partitioned into districts to be served by home health nurses, such that workload is balanced across districts and nurse travel is minimized. A set partitioning model for HHND is formulated and a column generation heuristic is developed which integrates ideas from optimization and local search. Methods for estimating district travel and workload are developed and implemented within the heuristic, which outperforms local search on test instances.
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Schiele, Julian [Verfasser], and Jens O. [Akademischer Betreuer] Brunner. "AI-Enabled Decision Support in Health Care / Julian Schiele ; Betreuer: Jens O. Brunner." Augsburg : Universität Augsburg, 2020. http://d-nb.info/1217194029/34.

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Nayfeh, Ayah. "Advance Care Planning for Mechanical Ventilation: Health Care Providers' Perspectives on Cross-Cultural Care." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31837.

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Background: Advance care planning (ACP) is a method used for patients to express in advance their preferences, beliefs and values for life-sustaining treatments at the end-of-life. With growing ethnocultural diversity in Canada, health care providers are managing an increasing number of diverse beliefs/values that are commonly associated with preferences for mechanical ventilation (MV) at the end-of-life. The aim of this project is to explore methods used by health care providers to set care plans for MV with ethnocultural populations. Methods: Qualitative analysis of semi-structured interviews with open-ended questions and two clinical vignette components was conducted with eight (8) health care providers who engage in ACP. Participants were recruited using a snowball-sampling approach from five acute-care hospitals within the Ottawa region. Results: Three major themes emerged from collected dataset: 1) Goals of care across illness trajectories, 2) Respecting beliefs, values, and wishes for care, and 3) Cross-cultural support in ACP. Using a value-based approach in ACP was described as an effective method for managing and interpreting diverse beliefs/values that impact decisions for MV. However, organizational, systemic, and personnel barriers that exist continue to hinder the provision of cross-cultural ACP across health settings. Contexte: La planification préalable des soins (PPS) est une méthode utilisée par les patients et les familles pour exprimer à l'avance leurs préférences liées aux traitements de prolongation de vie. En raison de la diversité ethnoculturelle croissante au Canada, les professionnels de la santé sont confrontés à des croyances et valeurs différentes, souvent associées à une préférence pour l’initiation et le maintien de la ventilation mécanique (VM) en fin de vie. L'objectif de ce projet consiste à explorer les stratégies utilisées par les professionnels de la santé lors des discussions associées à la VM auprès d'une clientèle multiculturelle. Méthodes: Huit (8) participants (médecins et infirmières) impliqués dans la PPS ont accepté de participer à une entrevue semi-structurée avec des questions ouvertes et deux scénarios cliniques. Les participants ont été recrutés à l'aide de la méthode d’échantillonnage par réseau (« boule de neige ») de cinq hôpitaux de la région d'Ottawa. Résultats: Trois grands thèmes ont émergé des entrevues: 1) les objectifs de soins à travers les trajectoires de la maladie, 2) le respect des croyances, valeurs et souhaits pour les soins, et 3) le soutien dans la PPS en contexte interculturel. Lors de la PPS, utiliser une approche basée sur les valeurs a été décrite comme une méthode efficace pour interpréter et prendre en compte les diverses croyances et valeurs qui ont une influence sur les décisions liées à la VM. Cependant, les barrières systémiques, organisationnelles et personnelles continuent d'entraver les services associés à la PPS en contexte interculturel dans les établissements de santé.
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Bertsch, Kylie M. "Day-of-Discharge Planning at Acute Care Hospitals." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1405077734.

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Westman, Göran. "Planning primary health care provision : assessment of development work at a health centre." Doctoral thesis, Umeå universitet, Socialmedicin, 1986. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-100557.

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At the Primary Health Care Centre in Vännäs (VPHCC), northern Sweden, a development work was implemented in 1976-1980. The overall purpose was to enhance primary health care planning. In trying to improve health care delivery cooperation with community members was initiated and some organizational changes like a new appointment system, a new medical record and local care programs for some common diseases were introduced. Official statistics were also used for comparative purposes. The aims of the work were postulated (increased accessibility, higher continuity, more equitable distribution and enhanced cooperation) and suitable methods were designed. From postal surveys, chartreviews and administrative data (from hospitals, out-patient clinics and health centres) figures and information were collected. Accessibility was studied by waiting room time which was reduced and continuity, analyzed with a new concept - visit based provider continuity - was improved. The question of equitable distribution was studied by the consultation rates at different out-patient clinics. It seemed as if the local development work changed the patterns of utilization but some important issues were not decisively answered. Repeated postal surveys reflected the question of equitable distribution and the cooperation between the VPHCC and the community members. Positive responses were recorded in aspects like telephone accessibility and health care information. In a tracer study of diabetes the quality of care was studied. The local care program was actually implemented in the daily practice but the question of care quality needs further penetration. Within the frames of the development work new methods in the health care planning were introduced. Our work started from the prerequisits of the VPHCC and other health centres might find other ways of planning for care provision. On a general level, however, the structure of our work - defining aims, means and evaluation methods - can be used by others.

Diss. (sammanfattning) Umeå : Umeå universitet, 1986, härtill 6 uppsatser.


digitalisering@umu
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Prater, Laura C. prater. "Advance Care Planning: Implications for Health Care Quality at the End of Life." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534344349446923.

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Buys, Lüet Schraader. "Bridging the divide between primary health care and community." Master's thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/22998.

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South African cities have a complex social and physical post-Apartheid layering. The historical legacy, referring here specifically to the inadequate roll-out of public facilities in areas and uprooting as well as separating of communities, have resulted in under serviced environments that can lack social cohesion and often struggle with poverty. Public institutions play a catalytic role within a community. To this end, health care portrays the government in a legible 'provider' role and is, in some ways, an obvious way to make citizens feel valued in comparison with other public institutions. Health care institutions impact the community in a unique way due to the combination of specificity of service and the emotive way it is experienced by the individual. This dissertation aims to research, define (and ultimately) test a strategy that aims to stitch together the fissure between community and institutions, by rethinking the urban interface of generic primary health care facilities. This research is structured around themes of theory, policy, the continuum of care and physical environments; each in order to better understand what and how the 'gap' between health care institution and community is constructed. Programmatic and/or spatial ideas that inform the architectural design. This dissertation asserts that providing 'traditional' generic institutions sustains rather than improves the life of the community. The research suggests that existing health care facilities can be more effective as public spaces by introducing new programmes, disaggregating the formal interface, redefining and activating a new urban threshold and providing meaningful open space. The design ultimately aims to act as a new skin or threshold through which institutions relate to the community.
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Thompson, Helen V. STRATEGY HEALTH CARE FACILITIES REQUIREMENTS DEPARTMENT OF DEFENSE INFORMATION SYSTEMS HEALTH DEPTH PLANNING EXECUTIVES WARTIME STANDARDS MILITARY MEDICINE MEDICINE THESES NAVAL PERSONNEL PEACETIME AUTOMATION. "Navy Health Care Strategic Planning Process : a draft functional description /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA273190.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, September 1993.
Thesis advisor(s): William J. Haga ; Magdi Kamel. "September 1993." Bibliography: p. 90. Also available online.
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Thompson, Helen V., and HEALTH CARE FACILITIES REQUIREMENTS DEPARTMENT OF DEFENSE INFORMATION SYSTEMS HEALTH DEPTH PLANNING EXECUTIVES WARTIME STANDARDS MILITARY MEDICINE MEDICINE THESES NAVAL PERSONNEL PEACETIME AUTOMATION STRATEGY. "Navy Health Care Strategic Planning Process: a draft functional description." Thesis, Monterey, California. Naval Postgraduate School, 1993. http://hdl.handle.net/10945/26648.

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This thesis explores the Navy Health Care Strategic Planning Process (NHCSPP) and attempts to apply the Department of Defense Automated Information Systems (AIS) Documentation Standard (DOD-STD-7935A) to develop a draft a functional description for the automation of the NHCSPP as module of the Navy Medical Executive Information System. The thesis begins with a discussion of Wartime and Peacetime Health Care Planning. This is followed by an in depth evaluation of the Navy Health Care Strategic Planning Process. The Navy Medical Executive Information System is then discussed, followed by the Functional Description Overview. The research indicates that Navy Health Care Strategic Planning is an extremely complex and intricate process and as such, traditional methodologies that emphasize capturing and representing users requirements upfront, i.e. DOD-STD-7935A, are not appropriate for automating the planning process. Additionally, the health care planning process needs to be standardized across all branches of the armed services. It is further ended that Navy Medicine create a workgroup of end-users and functional experts to develop a more detail functional description
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Books on the topic "AI health care planning"

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Klein, Burton R. Health care facility planning & construction. New York: Van Nostrand Reinhold, 1989.

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Melson, Kathryn A. Maternal infant health care planning. 2nd ed. Springhouse, Pa: Springhouse Corp., 1995.

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Melson, Kathryn A. Maternal infant health care planning. 2nd ed. Springhouse, Pa: Springhouse Corporation, 1994.

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Association, Canadian Hospital, ed. Disaster planning for health care facilities. Ottawa: Canadian Hospital Association Press, 1991.

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Evensky, Harold. Planning for long-term health care. Boston: Houghton Mifflin Co., 1992.

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L, Martin Leonide, and Siddall Sandra, eds. Nurses' role in health care planning. Norwalk, Conn: Appleton-Century-Crofts, 1986.

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Evensky, Harold. Planning for long-term health care. Boston: Houghton Mifflin Co., 1990.

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Cushing, Leo J. Medicaid and health care planning update. Boston, MA (Ten Winter Place, Boston 02108): MCLE, 2008.

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xuan, Chen nian. Ai ℗ʺSPA. Bei jing: Ke xue chu ban she, 2004.

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Basu, Salil Kumar. Genetic disorders and health care. Delhi: Shree Kala Prakashan, 1994.

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Book chapters on the topic "AI health care planning"

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Hancock, Christine. "Planning Health Care." In Surviving in General Management, 1–20. London: Macmillan Education UK, 1990. http://dx.doi.org/10.1007/978-1-349-10599-1_1.

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Mooney, Gavin H., Elizabeth M. Russell, and Roy D. Weir. "Planning and Health Care." In Choices for Health Care, 139–56. London: Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-18252-7_10.

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Johnston, Claire, and Kate Brown. "Delivering and planning home care." In Community Health Care, 46–62. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-14009-1_5.

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Wiemann, Constance M., and Albert C. Hergenroeder. "Employing Healthcare Transition Planning Tools." In Health Care Transition, 129–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72868-1_14.

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Abbatt, Fred, and Rosemary McMahon. "Planning the Teaching." In Teaching Health-Care Workers, 72–85. London: Macmillan Education UK, 1985. http://dx.doi.org/10.1007/978-1-349-18046-2_7.

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Abbatt, Fred, and Rosemary McMahon. "Planning the Assessment." In Teaching Health-Care Workers, 86–102. London: Macmillan Education UK, 1985. http://dx.doi.org/10.1007/978-1-349-18046-2_8.

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Iqbal, Sajid, Mehreen Tariq, Hareem Ayesha, and Noor Ayesha. "AI Technologies in Health-care Applications." In Artificial Intelligence and Internet of Things, 3–44. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003097204-2.

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Adams, Robert. "Planning." In Foundations of Health and Social Care, 293–98. London: Macmillan Education UK, 2007. http://dx.doi.org/10.1007/978-0-230-22933-4_31.

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Baggott, Rob. "Health Care Policy, Planning and Management." In Health and Health Care in Britain, 157–87. London: Macmillan Education UK, 2004. http://dx.doi.org/10.1007/978-1-137-11638-3_7.

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Sobolev, Boris, Victor Sanchez, and Lisa Kuramoto. "Discharge Planning." In Health Care Evaluation Using Computer Simulation, 347–58. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2233-4_19.

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Conference papers on the topic "AI health care planning"

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Mate, Aditya. "AI for Planning Public Health Interventions." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/682.

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Several scenarios involving public health interventions have a unifying underlying theme, that deals with the challenge of optimizing the limited intervention resources available. My dissertation casts this as a Restless Multi-Armed Bandit (RMAB) planning problem, identifying and addressing several new, fundamental questions in RMABs.
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H. Gyldenkaerne, Christopher, Gustav From, Troels Mønsted, and Jesper Simonsen. "PD and The Challenge of AI in Health-Care." In PDC '20: Participatory Design Conference 2020 - Participation Otherwise. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3384772.3385138.

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Wu, Chongke, Jeno Szep, Salim Hariri, Nimit Agarwal, Sumit Agarwal, and Carlos Nevarez. "SeVA: An AI Solution for Age Friendly Care of Hospitalized Older Adults." In 14th International Conference on Health Informatics. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010313605830591.

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Koh, Myung Suk, Young Suk Lim, and Mihyang Cha. "Investigation of Contraceptive Methods for Family Planning of a district, Uganda." In Health Care and Nursing 2015. Science & Engineering Research Support soCiety, 2015. http://dx.doi.org/10.14257/astl.2015.88.39.

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Berchi, Roberto, Giancarlo Fontana, Andrea Pagliosa, Rodolfo Bonora, and Giovanni Sesana. "A five steps methodology for ambulance planning." In 2010 IEEE Workshop on Health Care Management (WHCM). IEEE, 2010. http://dx.doi.org/10.1109/whcm.2010.5441262.

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Nelson, D. "221. The Role of Ecological Risk Assessment in Strategic Planning and Priority Setting." In AIHce 1996 - Health Care Industries Papers. AIHA, 1999. http://dx.doi.org/10.3320/1.2764887.

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Popper, Nikolas, and Felix Breitenecker. "Planning and Management of Health Care Systems by Simulation." In University for Business and Technology International Conference. Pristina, Kosovo: University for Business and Technology, 2018. http://dx.doi.org/10.33107/ubt-ic.2018.104.

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Geng, Na, Vincent Augusto, Xiaolan Xie, and Zhibin Jiang. "Planning and advance cancellation of MRI examinations of stroke patients." In 2010 IEEE Workshop on Health Care Management (WHCM). IEEE, 2010. http://dx.doi.org/10.1109/whcm.2010.5441279.

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Triki, Nizar, Thierry Garaix, and Xiaolan Xie. "A two-phase approach for periodic home health care planning." In 2014 IEEE International Conference on Automation Science and Engineering (CASE). IEEE, 2014. http://dx.doi.org/10.1109/coase.2014.6899375.

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Valente, Roberto, Angela Testi, Elena Tanfani, Marco Fato, Ivan Porro, Maurizio Santo, Gregorio Santori, Giancarlo Torre, and Gianluca Ansaldo. "Efficiency and priority in planning surgical sessions by using the SWALIS model." In 2010 IEEE Workshop on Health Care Management (WHCM). IEEE, 2010. http://dx.doi.org/10.1109/whcm.2010.5441258.

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Reports on the topic "AI health care planning"

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Hardie, Tom, Tim Horton, and Will Warburton. Switched on: How do we get the best out of automation and AI in health care? The Health Foundation, 2021. http://dx.doi.org/10.37829/hf-2021-i03.

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Appleford, Gabrielle, and Saumya RamaRao. Health financing and family planning in the context of Universal Health Care: Connecting the discourse. Population Council, 2019. http://dx.doi.org/10.31899/rh6.1021.

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Skufca, Laura. Planning for Health Care Costs in Retirement: A 2014 Survey of 50+ Workers. AARP Research, September 2014. http://dx.doi.org/10.26419/res.00088.001.

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Appleford, Gabrielle, and Saumya RamaRao. Health financing and family planning in the context of universal health care: Connecting the discourse in Kenya. Population Council, 2019. http://dx.doi.org/10.31899/rh6.1022.

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Klein, Sarah Klein, Douglas McCarthy McCarthy, and Alexander Cohen Cohen. Grand Rapids and West Central Michigan: Pursuing Health Care Value through Regional Planning, Cooperation, and Investment. New York, NY United States: Commonwealth Fund, April 2014. http://dx.doi.org/10.15868/socialsector.25103.

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Mahmood, Arshad, and Syeda Naz. Assessment of management information system [MIS] of national program for family planning and primary health care [LHW Program]. Population Council, 2012. http://dx.doi.org/10.31899/rh2.1028.

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Billings, Deborah, Eliana Del Pozo, and Hugo Arevalo. Testing a model for the delivery of emergency obstetric care and family planning services in the Bolivian public health system. Population Council, 2003. http://dx.doi.org/10.31899/rh4.1124.

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Nyarko, Philomena E., Fiifi Amoako-Johnson, Peter Atkinson, Faustina Nii-Amoo Frempong-Ainguah, Francis Dodoo, Jane C. Falkingham, Peter Gething, and Matthews Zoe. Evaluating the Impact of the Community-Based Health Planning and Services Initiative on Uptake of Skilled Birth Care in Ghana. Unknown, 2010. http://dx.doi.org/10.35648/20.500.12413/11781/ii113.

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Dy, Sydney M., Julie M. Waldfogel, Danetta H. Sloan, Valerie Cotter, Susan Hannum, JaAlah-Ai Heughan, Linda Chyr, et al. Integrating Palliative Care in Ambulatory Care of Noncancer Serious Chronic Illness: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), February 2020. http://dx.doi.org/10.23970/ahrqepccer237.

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Objectives. To evaluate availability, effectiveness, and implementation of interventions for integrating palliative care into ambulatory care for U.S.-based adults with serious life-threatening chronic illness or conditions other than cancer and their caregivers We evaluated interventions addressing identification of patients, patient and caregiver education, shared decision-making tools, clinician education, and models of care. Data sources. We searched key U.S. national websites (March 2020) and PubMed®, CINAHL®, and the Cochrane Central Register of Controlled Trials (through May 2020). We also engaged Key Informants. Review methods. We completed a mixed-methods review; we sought, synthesized, and integrated Web resources; quantitative, qualitative and mixed-methods studies; and input from patient/caregiver and clinician/stakeholder Key Informants. Two reviewers screened websites and search results, abstracted data, assessed risk of bias or study quality, and graded strength of evidence (SOE) for key outcomes: health-related quality of life, patient overall symptom burden, patient depressive symptom scores, patient and caregiver satisfaction, and advance directive documentation. We performed meta-analyses when appropriate. Results. We included 46 Web resources, 20 quantitative effectiveness studies, and 16 qualitative implementation studies across primary care and specialty populations. Various prediction models, tools, and triggers to identify patients are available, but none were evaluated for effectiveness or implementation. Numerous patient and caregiver education tools are available, but none were evaluated for effectiveness or implementation. All of the shared decision-making tools addressed advance care planning; these tools may increase patient satisfaction and advance directive documentation compared with usual care (SOE: low). Patients and caregivers prefer advance care planning discussions grounded in patient and caregiver experiences with individualized timing. Although numerous education and training resources for nonpalliative care clinicians are available, we were unable to draw conclusions about implementation, and none have been evaluated for effectiveness. The models evaluated for integrating palliative care were not more effective than usual care for improving health-related quality of life or patient depressive symptom scores (SOE: moderate) and may have little to no effect on increasing patient satisfaction or decreasing overall symptom burden (SOE: low), but models for integrating palliative care were effective for increasing advance directive documentation (SOE: moderate). Multimodal interventions may have little to no effect on increasing advance directive documentation (SOE: low) and other graded outcomes were not assessed. For utilization, models for integrating palliative care were not found to be more effective than usual care for decreasing hospitalizations; we were unable to draw conclusions about most other aspects of utilization or cost and resource use. We were unable to draw conclusions about caregiver satisfaction or specific characteristics of models for integrating palliative care. Patient preferences for appropriate timing of palliative care varied; costs, additional visits, and travel were seen as barriers to implementation. Conclusions. For integrating palliative care into ambulatory care for serious illness and conditions other than cancer, advance care planning shared decision-making tools and palliative care models were the most widely evaluated interventions and may be effective for improving only a few outcomes. More research is needed, particularly on identification of patients for these interventions; education for patients, caregivers, and clinicians; shared decision-making tools beyond advance care planning and advance directive completion; and specific components, characteristics, and implementation factors in models for integrating palliative care into ambulatory care.
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Foreit, James R. Postabortion family planning benefits clients and providers. Population Council, 2005. http://dx.doi.org/10.31899/rh16.1006.

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A woman’s fertility can return quickly following an abortion or miscarriage, yet recent data show high levels of unmet need for family planning (FP) among women who have been treated for incomplete abortion. This leaves many women at risk of another unintended pregnancy and in some cases subsequent repeated abortions and abortion-related complications. It is thus vital for programs to provide a comprehensive package of postabortion care (PAC) services that includes medical treatment, FP counseling and services, and other reproductive health services such as evaluation and treatment for sexually transmitted infections, HIV counseling and/or testing, and community support and mobilization. Providing FP services within PAC benefits clients and programs. Facilities that can effectively treat women with incomplete abortions can also provide contraceptive services, including counseling and appropriate methods. As stated in this brief, any provider who can treat incomplete abortion can also provide selected FP methods. Clients, providers, and programs benefit when FP methods are provided to postabortion clients at the time of treatment.
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