Impact of technology on patient discharge decision making

Approximately 20 to 25 percent of patients discharged from primary healthcare facilities are readmitted within 30 days at a cost of roughly $42 billion dollars per year to insurance providers. Accountable Care Organizations (ACOs) create a network of healthcare providers aimed at improving the quality of patient care within a new 'pay for performance' business model. The Affordable Care Act (ACA) of 2010 directed the ACOs to establish new accounting practices including financial penalties for unplanned 30-day readmissions. Some unplanned patient readmissions can be caused by inappropriate interventions and in others, patients were unable to comply due to numerous complex social and technical complications. Incentives within the ACA for adoption of electronic health records (EHR) has motivated the rapid creation and adoption of new complementary predictive risk and decision technologies aimed at enhancing discharge decision processes. At least 26 unique risk prediction technologies of varying predictive nature have been created. New technologies are often proposed without methods to guide their design or implementation. The impacts of inserting a new patient discharge risk technology into an expert heuristic-based decision process are not well defined, nor are the acceptance levels of that technology in a highly trained group of healthcare professionals. Research conducted on heuristics and cognitive biases within the healthcare industry is not particular to patient discharge care management, and has not been assessed since the ACA was implemented. This research will present new knowledge about risk technology impacts on expert heuristics and cognitive biases while examining the acceptance of these technologies. Simultaneously, the research presents a methodology rooted in cognitive task analysis methods to analyze current discharge systems and guide training design strategies for health care professionals towards enhancing the quality of patient discharge care

Trust in Medical Artificial Intelligence: A Discretionary Account

This paper sets out an account of trust in AI as a relationship between clinicians, AI applications, and AI practitioners in which AI is given discretionary authority over medical questions by clinicians. Compared to other accounts in recent literature, this account more adequately explains the normative commitments created by practitioners when inviting clinicians’ trust in AI. To avoid committing to an account of trust in AI applications themselves, I sketch a reductive view on which discretionary authority is exercised by AI practitioners through the vehicle of an AI application. I conclude with four critical questions based on the discretionary account to determine if trust in particular AI applications is sound, and a brief discussion of the possibility that the main roles of the physician could be replaced by AI

Emotions, Intuitions and Risk Perception in Critical Care

The theory of decision-making as it applies to bioethics and healthcare assumes a rational decision maker: someone who knows all his alternatives, has clear preferences, can rank and weigh risks and benefits of an intervention, and always acts in his own best interests. However, the growing body of research from the field of decision science shows that, in reality, such a purely rational decision maker does not exist. Instead, patients are rational within personal or environmental constraints such as uncertainty or ambiguity in which non-rational approaches such as emotion and intuition are instrumental. This issue is particularly important in critical care. To ensure that patients receive the end-of-life care that they want, especially considering the increase in futile care, proper risk communication is necessary. While the move from paternalism to the current emphasis on patient empowerment and shared decision-making means that patients and surrogates want comprehensive and understandable information about their conditions and treatment in order to participate fully in decisions about their care, emotions complicate this decision-making. Though there is a great deal of empirical research on emotions and risk perception, there is a lack of philosophical research on this topic, especially when it comes to futility considerations in critical care. This research asserts that emotions should be considered a necessary component of ethical assessment of risk and communication about risk, especially in the field of critical care. It explores the existing literature on how people employ emotions and deliberation in their decision-making, and it questions the existing bias among normative scholars that decisions resulting from deliberation are inherently better or superior to those grounded in intuition. Furthermore, this research attempts to determine the value of autonomy in designing health policies grounded in behavioral economics. While providers want patients to make decisions that promote their own interests, this task is rarely achieved when patients are left alone to make important decisions. This research questions whether providers should let their patients make decisions that divert them from their own health goals or intervene by actively directing patients toward choices that are most likely to promote their goals

Multidisciplinary perspectives on Artificial Intelligence and the law

This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

Mortality case note review use for hospital care quality improvement: A methodological, psychological and qualitative investigation

Evaluating hospital care quality using case-note reviews is mandated in the United Kingdom and is endorsed by many high-income countries. This thesis separately addresses both the validity of case-note reviews and the use of case-note reviews for care quality improvement. On case-note reviewing validity, there are moderate-to-high levels of disagreement (variability) between multiple clinician case-note reviewers when evaluating the overall care quality of the same case-note. The sources of this disagreement (variability) are unknown. On case-note review use, the potential factors which affect case-note reviewing in hospitals has not been well-studied in relation to their contribution to hospital care quality improvement. This thesis presents the findings of three original studies and seeks to both identify the sources for this reviewer variability and the organizational factors which influence case-note review’s likely contribution to hospital quality improvement. The introduction discusses the policy context and offers a critique of hospital mortality statistics with the prospective use of case-note reviews as an alternative approach for detecting care quality issues. Chapter 1 involved a systematic review of preventable mortality rates and a characterization of their measurement properties for evaluating care quality and subsequent hospital ranking. Findings concluded that a limitation of studies not accounting for variation between different hospitals, assuming equal variance, in the ranking process. Case-note reviews are presented as a workable alternative, to which this thesis is devoted to investigating. Chapter 2 presents the findings of an original systematic review which identified cognitive biases and heuristics related to case-note review care quality judgements. Cognitive biases and heuristics, sourced from two systematic reviews, are investigated with their plausible influence upon case-note reviewer care quality judgments using clinical scenarios derived using a systematic literature search and informed by a panel consensus. Findings indicate the plausible influence of cognitive biases and heuristics. Chapter 3 investigates the influence of reviewer attitudes; their demographics and patient case-note review characteristics upon case-note reviewer care quality judgements. Selected attitudes did not significantly influence care quality judgements and a significant proportion of care quality judgement variability is unexplained by the included independent variables. Chapter 4 describes case study fieldwork in an acute NHS Trust which explored the organizational processes around case-note review including its embedding, information flow and its perceived quality improvement contribution. We found that case-note reviews were well-embedded, with there being limited information flow from ward-to-board. Chapter 5 is a critical reflection of the research process and the assumptions made in this thesis. Chapter 6 summarizes the thesis, discusses practical implications, and identifies opportunities for future research for quality improvement from case-note reviews

An evaluation of the feasibility to use digital solutions to support the provision of healthcare in the NHS

In England, there is an increase in prevalence of patients with long-term conditions (LTC) with approximately 26 million people having at least one LTC, utilising 70% of the total health and social budget. The Five Year Forward View and the NHS Long Term plan have identified that digital healthcare is a cost-effective technology that has the potential to integrate systems, improve efficiencies and have better clinical and social outcomes for patients. Whilst the successful implementation of digital healthcare in the NHS has been challenging, the severe acute respiratory syndrome coronavirus-2 (COVID-19) has been a major driver to support the rapid implementation of digital technology to safely maintain services and public health. This thesis aimed to evaluate the feasibility of using digital solutions to support the provision of healthcare in the borough of Croydon. Overall, a mixed-method approach consisting of quantitative and qualitative techniques was used to investigate the usability and acceptability of three technology enabled care solutions: a telehealth monitoring system in care home with residents with dementia, a follow up telephonic solution post-discharge and a digital stethoscope to screen for congenital murmurs. The study that assessed the potential role of telehealth system for monitoring residents with dementia living in a care home took place over three six-month phases [control phase (CP), active monitoring phase (AMP) and active monitoring with text alerts (AMTAP)]. The solution involved carers recording vital signs and completing health assessment questions. In AMTAP, the early warning triage system generated text alerts when abnormal responses or vital signs were detected. Twenty-seven residents participated during the CP and AMP whilst only fourteen residents participated during AMTAP. The quantitative section of this study calculated the frequency effect of the telehealth solution on the general practitioner visits (GPV), antibiotic prescribing (AP), emergency department (ED) visits and hospital inpatient (IP) events whilst the qualitative section of this study explored multidisciplinary healthcare professionals (HCPs) awareness and experiences of the telehealth system and reported on their perceptions pre and post implementation. The digital healthcare solution resulted in more frequent monitoring of residents’ vital signs (weekly vs monthly) resulting in an increase in frequency of GP (p=0.009) and AP (p<0.001) and a decrease in ED, IP and duration of an admission. Multidisciplinary HCPs were interviewed pre-AMP (n=33) and post-AMP (n=28). HCPs working with the digital solution reported increase knowledge and awareness after using the digital solution whereas HCPs who did not have direct contact reported a lack of understanding and awareness. Barriers to implementation included: lack of resources, training, inadequate staffing, equipment failure and poor system implementation. Indirect beneficial outcomes included: improved medication changes for residents and improved professional satisfaction and therefore HCPs wanted to use digital healthcare in the future. The second study evaluated a post discharge telephonic intervention by two nurses in the role of discharge advocates (DA) to ensure that the patients received the required post discharge care. The intervention was part of a funded project aiming to design a readmission prediction risk score system (OPTIMAL) to identify patients in need of a post discharge intervention to prevent a readmission within 30 days of discharge. The DA recruited eligible patients into the intervention (n=740) and control arms (n=730). It was determined that a sample size of 265 patients’ evaluations per a group (total 530, [intervention (n=265) and control (n=265)]) would be needed to determine patients’ satisfaction levels at 95% confidence interval. The sample extracted for evaluation had a statistically significant (p=0.001) higher mean OPTIMAL 30-day readmission risk score than the study arms. The OPTIMAL risk score of admission was reflective of the actual rate of re-admission with an average predictive score of 16.28% and actual 30-day readmission rate of 15.12% (n=223) for the whole sample. In the sample tested, the OPTIMAL predictive percentage readmission scores were 20.27% and 20.91% in the evaluation and control arms compared to (n=61, 23%) actual 30 days readmission rate across both arms. There was no significant difference in 30-day readmission rate between the study control and intervention arms. However, the percentage actual readmission rate was statistically significantly lower for the intervention evaluation group (9.4%) as compared to the control evaluation group (13.5%) (p<0.001). The DA call was perceived positively by patients as they felt that the DA understood their health status (81.1%) and was quite helpful (82.2%). Based on the findings, perhaps the intervention should not have been offered to all patients but to those that will most benefit from it, so targeted intervention based on the OPTIMAL readmission risk score, as the intervention did statistically significantly lower readmission rate for those patients. Lastly, an investigation was conducted to evaluate both the feasibility of an electronic stethoscope for the detection of congenital heart murmurs and its usability and associated software amongst clinicians with different levels of experience. Paediatric patients (n=72) with suspected murmurs attending a Paediatric Cardiologist led outpatient clinic and forty age-matched participants with no discernible murmurs consented to 30-second heart sound Consultant Paediatrician verified recordings using a 3M™ Littmann® Electronic Stethoscope Model 3200 to create a heart sound database. MATLAB (The MathWorks Inc., USA) was used to create sound waveforms and the 3M™ Littmann Steth Assist Heart and Lung Sound Visualization Software® was used to record and playback heart sounds. For the recordings without murmurs (n=6), the waveform between heart sounds appeared regular and smooth whereas in the recordings with murmurs (n=6) the waveforms between heart sounds had varying frequency with some higher frequency components. This was apparent with grade 3 and above murmurs, but this was less apparent in grade one and two murmurs, hence not proving a useful tool for screening. Clinicians (n=38) retrospective assessment of heart sounds played through a Bluetooth speaker resulted in system sensitivity of 77% and a specificity of 69%. The ability to distinguish between a normal and abnormal heart sound when listening to the audio samples was related to the experience of the clinician, with consultants scoring the highest. Unfortunately, clinicians (61%) reported that they would not be comfortable to confirm a diagnosis remotely using the system. All clinicians were able to acquire heart sounds using the electronic stethoscope. However, only Consultants (n=11), Senior House Officers (SHOs) and Registrars (n=4) ranked the electronic stethoscope with an acceptable System Usability Scale (SUS) score (≥70). Clinicians identified advantages for the system, with potential use as an educational tool and for the retrospective review of heart sounds. The three studies above evaluated the feasibility of using digital solutions to support the provision of healthcare. The evaluation has proven that digital solutions have the potential to support HCPs in healthcare provision, but the technology, organisation and patients need to be considered so that the proper ‘digital fit’ can be achieved to ensure that digital solutions are adopted by HCPs and that patients can experience the full benefits from them for both their healthcare and social outcomes

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

Aspects of registered psychaitric nurses' talk about their clinical judgement and decision-making.

Researchers have been investigating the clinical judgement and decision-making of nurses for several decades now. However, prior to the research described in this thesis, Registered Psychiatric Nurses (RPNs) working in the Irish Republic had not been the subject of a comprehensive study looking specifically at their clinical judgement and decision-making. As this is the first study conducted in this area, it takes an exploratory descriptive approach. With a comprehensive review of the literature and pilot study (n=7) as its basis, a novel mixed methods study was designed. Simulated cases presented in an audio-visual format were used to collect in-vivo and retrospective data in the form of narratives from participants (n=40) across the Irish Republic. The sample comprises RPNs across all levels of experience working in several sites, representing the full range of Irish mental health services. The data were analysed using comparative keyword analysis and conversation analysis informed discursive analysis. Quantitative and qualitative analysis reveals participants’ judgement and decision making to be routinised and habitual, hinging on reference to typicality grounded mainly in psychiatric diagnoses. The role of participants can be seen to represent the paternalism of the social order of which it is part. Participants express confidence and certainty in their judgement and decision-making, even where paraverbal and other discursive evidence points towards situations characterised by uncertainty. The study’s findings are of particular interest given the direction envisaged for the profession of psychiatric nursing by leading academics, health service providers and professional and statutory bodies. The findings of this study suggest that if psychiatric nursing in the Irish Republic is to proceed towards more person-centred, autonomous practice with a stronger therapeutic focus, dramatic restructuring of psychiatric nurses’ roles will be required. In conclusion, the thesis discusses this situation with reference to the challenges made evident by the study, along with the viable options available to address them

1993-1995-UNM CATALOG

Course catalog for 1993-1995https://digitalrepository.unm.edu/course_catalogs/1096/thumbnail.jp