Exploration of the Clinical Utility of High Risk Medication Regimens

University of Minnesota Ph.D. dissertation. November 2014. Major: Health Informatics. Advisor: Bonnie Westra. 1 computer file (PDF); vii, 124 pages.Title: Exploration of the Clinical Utility of High Risk Medication Regimens Background: Unnecessary hospital readmissions are a costly problem for the U.S. health care system. An automated algorithm was developed to target this problem and proven to predict elderly patients at greater risk of re-hospitalization based on their medication regimens. Objective: Create an automated algorithm for predicting elderly patients' medication-related risks for re-hospitalization (study 1), optimize the algorithm by improving the sensitivity of its medication criteria (study 2), and determine its usefulness within different patient populations (study 3). Materials and methods: Outcome and Assessment Information Set (OASIS) and medication data were reused from a previous, manual study of 911 patients from 15 Medicare-certified home health care agencies. Medication data was converted to standardized drug codes using APIs managed by the National Library of Medicine (NLM), and then integrated in an automated algorithm that calculations patients' high risk medication regime scores (HRMRs). A comparison of results between the automated and manual processes was conducted to determine HRMR score match rates (study 1). Odds Ratio analyses, literature reviews and clinical judgments were used to adjust the scoring of patients' High Risk Medication Regimens (HRMRs). Receiver Operating Characteristic (ROC) analysis evaluated whether these adjustments improved the predictive strength of the algorithm (study 2). Unsupervised clustering was used to determine patient population subgroups. HRMR scores were then applied to these subgroups, and ROC & FDR analysis evaluated whether the predictive strength of the algorithm increased for a specific patient population subgroup (study 3). Results: HRMR scores are composed of polypharmacy (number of drugs), potentially inappropriate medications (PIM) (drugs risky to the elderly), and Medication Regimen Complexity Index (MRCI) (complex dose forms, instructions or administration). The automated algorithm produced polypharmacy, PIM and MRCI scores that matched with 99, 87, 99 percent of the scores, respectively, from the manual analysis (study 1). Strongest ROC results for the HRMR components were .68 for polypharmacy when excluding supplements; and .60 for PIM and .69 for MRCI using the original HRMR criteria (study 2). Subgroups consisting of males who have adult children as primary caregivers show stronger AUC curves than the entire population. (study 3). Conclusion: The automated algorithm can predict elderly patients at risk of hospital readmissions and is improved by a modification to its polypharmacy criteria. A hypothesis for future study includes that the algorithm is more predictive in the subgroup of males who have adult children as their caregiver

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Healthcare in the Smart Home: A Study of Past, Present and Future

Open Access journalUbiquitous or Pervasive Computing is an increasingly used term throughout the technology industry and is beginning to enter the consumer electronics space in its most recent form under the umbrella term: “Internet of Things”. One area of focus is in augmenting the home with intelligent, networked sensors and computers to create a Smart Home which opens a host of possibilities for the role of tomorrow’s dwelling. As the world’s population continues to live longer and consequently experience more medical-related ailments, at the same time institutional healthcare is struggling to cope, the role of the Smart Home becomes paramount to monitoring a dweller’s health and providing any necessary intervention. This study looks at the history of Smart Home Healthcare, current research areas, and potential areas of future investigation. Unique categorisations are presented in Activities of Daily Living (ADL) and Personal Sensors, and a thorough look at the application of Smart Home Healthcare is presented. Technology can augment traditional methods of healthcare delivery and in some cases completely replace it. Costs can be reduced and medical adherence can be increased, all of which contribute to a more sustainable and effective model of care

Developing a Tool to Support Decisions on Patient Prioritization at Admission to Home Health Care

Background and aims: Millions of Americans are discharged from hospitals to home health every year and about third of them return to hospitals. A significant number of rehospitalizations (up to 60%) happen within the first two weeks of services. Early targeted allocation of services for patients who need them the most, have the potential to decrease readmissions. Unfortunately, there is only fragmented evidence on factors that should be used to identify high-risk patients in home health. This dissertation study aimed to (1) identify factors associated with priority for the first home health nursing visit and (2) to construct and validate a decision support tool for patient prioritization. I recruited a geographically diverse convenience sample of nurses with expertise in care transitions and care coordination to identify factors supporting home health care prioritization. Methods: This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults referred to home health. Variables included sociodemographics, diagnosis, comorbid conditions, adverse events, medications, hospitalization in last 6 months, length of stay, learning ability, self-rated health, depression, functional status, living arrangement, caregiver availability and ability and first home health visit priority decision. A combination of data mining and logistic regression models was used to construct and validate the final model. Results: The final model identified five factors associated with first home health visit priority. A cutpoint for decisions on low/medium versus high priority was derived with a sensitivity of 80% and specificity of 57.9%, area under receiver operator curve (ROC) 75.9%. Nurses were more likely to prioritize patients who had wounds (odds ratio [OR]=1.88), comorbid condition of depression (OR=1.73), limitation in current toileting status (OR= 2.02), higher numbers of medications (increase in OR for each medication =1.04) and comorbid conditions (increase in OR for each condition =1.04). Discussion: This dissertation study developed one of the first clinical decision support tools for home health, the PREVENT - Priority for Home Health Visit Tool. Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes

“Let the algorithm decide”: is human dignity at stake?

The goal of this article is to argue that the debate regarding algorithmic decision-making and its impact on fundamental rights is not well-addressed and should be reframed in order to allow for adequate regulatory policies regarding recent technological developments in automation. A review of the literature on algorithms and an analysis of Articles 6, IX and 20 of the Brazilian Federal Law n° 13.709/2018 (LGPD) lead to the conclusion that claims that algorithmic decisions are unlawful because of profiling or because they replace human analysis are imprecise and do not identify the real issues at hand. Profiles are nothing more than generalizations, largely accepted in legal systems, and there are many kinds of decisions based on generalizations which algorithms can adequately make with no human intervention. In this context, this article restates the debate about automated decisions and fundamental rights focusing on two main obstacles: (i) the potential for discrimination by algorithmic systems and (ii) accountability of their decision-making processes. Lastly, the arguments put forward are applied to the current case of the covid-19 pandemic to illustrate the challenges ahead

M-health review: joining up healthcare in a wireless world

In recent years, there has been a huge increase in the use of information and communication technologies (ICT) to deliver health and social care. This trend is bound to continue as providers (whether public or private) strive to deliver better care to more people under conditions of severe budgetary constraint

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

Addressing data accuracy and information integrity in mHealth using ML

The aim of the study was finding a way in which Machine Learning can be applied in mHealth Solutions to detect inaccurate data that can potentially harm patients. The result was an algorithm that classified accurate and inaccurate data