Health Analytics to Manage Turbulence in Patient Flow: A Field Study of Transitions in Care Processes

The potential use of advanced data analytics in healthcare has seen significant interest in both research and practice. Fundamentally, the contribution of IS and analytics research in healthcare is to identify and assess the impact of interventions that can make a significant difference to the quality and cost of care. The American Heart Association (AHA) recently issued a scientific statement calling for research on heart failure transition care to identify impactful processes and practices. This paper presents our conceptualization of ingress and egress patient flow management to investigate the impact of transition care. The larger research question we attempt to address is: How can we identify and inform impactful transition of care interventions that manage demand uncertainty, and improve resource allocation and utilization, while providing improved quality of care for heart failure patients? We present preliminary results of text-mining and process analytics and discuss our plans for quasi-experimental validation

An M-Health Tool for Improved Self-Care of Heart Failure Patients: An On-Going Field Study

This paper describes an on-going field study to develop an eHealth and mHealth system to accurately monitor heart failure and guide appropriate actions. We describe the foundations of the tool to nurture appropriate self-care behaviors along with the theoretical development to serve as the foundation. The on-going research and research design is explained in detail and implications are discussed. Preliminary data collection and descriptive analysis are presented and on-going research plans are discussed. The research will develop and test the impact of the system on the quality of care for patients, the care processes for caregivers and presents implications for the cost of care. The research is guided by the hypothesis that the mHealth tool will impact the health care provider by reducing emergency department visits and same cause readmissions – both factors that significantly impact the cost and quality of care

APPLICATION OF PREDICTIVE ANALYTICS IN CUSTOMER RELATIONSHIP MANAGEMENT: A LITERATURE REVIEW AND CLASSIFICATION

This study is aimed to provide a comprehensive literature review and a classification scheme for application of predictive analytics and tools in customer relationship management (CRM). The application of predictive analytics in CRM is an emerging trend. PA methods help to analyze and understand customer behaviors and acquire and retain customers and also maximize customer value. Thus it facilitates CRM decisions making and supports development of CRM strategies in a customer-centric economy. This paper is aimed to present a comprehensive review of literature related to application of predictive analytics in CRM published in both academic and practitioner journals between 2003 and 2013

Using Electronic Health Records to Mitigate Workplace Burnout Among Clinicians During the COVID-19 Pandemic: Field Study in Iran

Background: The COVID-19 pandemic spread worldwide in 2020. Notably, in the countries dealing with massive casualties, clinicians have worked in new conditions characterized by a heavy workload and a high risk of being infected. The issue of clinician burnout during the pandemic has attracted considerable attention in health care research. Electronic health records (EHRs) provide health care workers with several features to meet a health system\u27s clinical needs. Objective: We aim to examine how the use of EHR features affects the burnout of clinicians working in hospitals that have special wards for confirmed COVID-19 cases. Methods: Using an online survey, we collected data from 368 physicians, physician assistants, and nurses working in six hospitals that have implemented EHRs in the city of Tehran in Iran. We used logistic regression to assess the association between burnout and awareness of EHR features, EHR system usability, concerns about COVID-19, technology solutions, hospital technology interventions, hospital preparedness, and professional efficacy adjusted for demographic and practice characteristics. Results: The primary outcome of our study was self-reported burnout during the COVID-19 pandemic. Of the 368 respondents, 36% (n=134) reported having at least one symptom of burnout. Participants indicated that the leading cause of EHR-related stress is inadequate training for using technology (n=159, 43%), followed by having less face-to-face time with patients (n=140, 38%). Positive perceptions about the EHR\u27s ease of use were associated with lower odds of burnout symptoms. More interventions, such as clear communication of regulations; transparency in policies, expectations, and goals regarding the use of technology in the clinical workflow; and hospital preparedness to cope with the challenges of the pandemic, were associated with lower odds of burnout. Conclusions: The use of EHR applications, hospital pandemic preparation programs, and transparent technology-related policies and procedures throughout the epidemic can be substantial mitigators of technology-based stress and clinician burnout. Hospitals will then be better positioned to devise or modify technology-related policies and procedures to support physicians\u27 and nurses\u27 well-being during the COVID-19 pandemic. Training programs, transparency in communications of regulations, and developing a clear channel for informing clinicians of changes in policies may help reduce burnout symptoms among physicians and nurses during a pandemic. Providing easily accessible mentorship through teleconsultation and 24-hour available information technology support may also help to mitigate the odds of burnout

Does Data Entry Structure Influence Sharing Health Information of Patients with Chronic Health Impairment?: An Experimental Study

The quality of the patient health information databases determines the success of health information exchange (HIE) networks. Data entry interfaces are suggested as an important factor affecting the quality of information. However, little is known about whether individuals with different diseases (mental and physical) care for the data entry structure in sharing health information. We conduct four experiments to examine the impact of different health problems (mental vs. physical) and types of data entry interfaces (structured vs. unstructured) on individuals\u27 willingness to share health information. Findings demonstrate that the disease type and degree of data entry structure significantly influence individuals\u27 perceptions of psychological risk, privacy concern, stigma, and willingness to share health information. This study suggests that the best level of structure for data entry interfaces could be designed at the point of care with respect to patients\u27 type of diseases in order to improve the success of HIE networks

Investigating the impact of health analytics on the cost and quality of care for patients with heart failure

The healthcare industry is under tremendous pressure to improve the quality of care and provide more patient centric care, while reducing costs. The potential use of data analytics to address these health system issues has raised significant interest in both research and practice. Health Analytics is central to informing and realizing the systematic quality improvements and cost reductions required by healthcare reform. Fundamentally, the contribution of IS and analytics research in healthcare is to identify and study the impact of interventions that can make a significant difference to the quality and cost of care. This dissertation is concentrated on patients with heart failure (HF). HF is the number one killer in the world, and is the largest contributor to healthcare costs in the United States. Moreover, HF is one of the six conditions used by the Centers for Medicare and Medicaid Services (CMS) to exercise fiduciary control over health systems by monitoring both the quality and cost of care. Specifically, my larger research question is “How can we identify and inform impactful transition of care interventions that manage costs and improve resource allocation efficiencies while providing improved quality of care for heart failure patients?” We adopted a mixed-method approach to study the impact of transitional care in a healthcare system for patients with heart failure. This dissertation includes three essays. In the first essay, I use qualitative methods to study the nature, sources and impacts of information coordination problems as HF patients’ transition through the patient flow in a health system. I propose a set of interventions based on my analysis of information and control errors along the continuum of care to inform the design of appropriate interventions that improve the cost and quality of care. In the second essay, I empirically evaluate the impact of these interventions on cost and quality of care measures such as all cause readmissions, heart failure readmissions, ER visits, length of stay, and cost of care. Analysis suggests that multicomponent complex transitional interventions have significant impact on reducing 30-day readmission and ER visits. The third essay is dedicated to understanding the impact of heart failure patient’s self-care behaviors. I developed and validated an assessment tool for patients with heart failure to monitor and score their condition accurately. Together, these essays investigate impactful transition of care interventions that can help healthcare organizations improve quality of care and manage costs from the clinical, administrative and patient perspectives

The Potential of Blockchain Technology for Health Information Exchange: Experimental Study From Patients’ Perspectives

Background: Nowadays, a number of mechanisms and tools are being used by health care organizations and physicians to electronically exchange the personal health information of patients. The main objectives of different methods of health information exchange (HIE) are to reduce health care costs, minimize medical errors, and improve the coordination of interorganizational information exchange across health care entities. The main challenges associated with the common HIE systems are privacy concerns, security risks, low visibility of system transparency, and lack of patient control. Blockchain technology is likely to disrupt the current information exchange models utilized in the health care industry. Objective: Little is known about patients’ perceptions and attitudes toward the implementation of blockchain-enabled HIE networks, and it is still not clear if patients (as one of the main HIE stakeholders) are likely to opt in to the applications of this technology in HIE initiatives. Thus, this study aimed at exploring the core value of blockchain technology in the health care industry from health care consumers’ views. Methods: To recognize the potential applications of blockchain technology in health care practices, we designed 16 information exchange scenarios for controlled Web-based experiments. Overall, 2013 respondents participated in 16 Web-based experiments. Each experiment described an information exchange condition characterized by 4 exchange mechanisms (ie, direct, lookup, patient-centered, and blockchain), 2 types of health information (ie, sensitive vs nonsensitive), and 2 types of privacy policy (weak vs strong). Results: The findings show that there are significant differences in patients’ perceptions of various exchange mechanisms with regard to patient privacy concern, trust in competency and integrity, opt-in intention, and willingness to share information. Interestingly, participants hold a favorable attitude toward the implementation of blockchain-based exchange mechanisms for privacy protection, coordination, and information exchange purposes. This study proposed the potentials and limitations of a blockchain-based attempt in the HIE context. Conclusions: The results of this research should be of interest to both academics and practitioners. The findings propose potential limitations of a blockchain-based HIE that should be addressed by health care organizations to exchange personal health information in a secure and private manner. This study can contribute to the research in the blockchain area and enrich the literature on the use of blockchain in HIE efforts. Practitioners can also identify how to leverage the benefit of blockchain to promote HIE initiatives nationwide

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Do Hospitals Need to Extend Telehealth Services? An Experimental Study of Different Telehealth Modalities during the COVID-19 Pandemic

Background The COVID-19 pandemic has changed health care systems and clinical workflows in many countries, including the United States. This public health crisis has accelerated the transformation of health care delivery through the use of telehealth. Due to the coronavirus\u27 severity and pathogenicity, telehealth services are considered the best platforms to meet suddenly increased patient care demands, reduce the transformation of the virus, and protect patients and health care workers. However, many hospitals, clinicians, and patients are not ready to switch to virtual care completely. Objectives We designed six experiments to examine how people (as an actual beneficiary of telehealth) evaluate five telehealth encounters versus face-to-face visits. Methods We used an online survey to collect data from 751 individuals (patients) in the United States. Results Findings demonstrate that significant factors for evaluating five types of telehealth encounters are perceived convenience expected from telehealth encounters, perceived psychological risks associated with telehealth programs, and perceived attentive care services delivered by telehealth platforms. However, significant elements for comparing telehealth services with traditional face-to-face clinic visits are perceived cost-saving, perceived time-saving, perceived hygienic services, perceived technical errors, perceived information completeness, perceived communication barriers, perceived trust in medical care platforms\u27 competency, and perceived privacy concerns. Conclusion Although the in-person visit was reported as the most preferred care practice, there was no significant difference between people\u27s willingness to use face-to-face visits versus virtual care. Nevertheless, before the widespread rollout of telehealth platforms, health care systems need to determine and address the challenges of implementing virtual care to improve patient engagement in telehealth services. This study also provides practical implications for health care providers to deploy telehealth effectively during the pandemic and postpandemic phases