Intelligent health in the IS area: A literature review and research agenda

As the global demand for healthcare services continues to grow, improving the efficiency and effectiveness of the healthcare ecosystem has become a pressing concern. Information systems are transforming the healthcare delivery process, shifting the focus of healthcare services from passive disease treatment to proactive health prevention and the healthcare management model from hospital-centric to patient-centric. This study focuses on reviewing research in IS journals on the topic of e-health and is dedicated to constructing a theoretical model of intelligent health to provide a research basis for future discussions in this field. In addition, as the innovation of intelligent healthcare services has led to changes in its elements (e.g., an increase in the number of stakeholders), there is an urgent need to sort out and analyze the existing research.

Scholarly communication between health informatics and information systems: A bibliometric study

The challenges of IT adoption in the healthcare sector have generated much interest across a range of research communities, including Information Systems (IS) and Health Informatics (HI). Given their long-standing interest in IT design, development, implementation, and adoption to improve productivity and support organisational transformation, the IS and HI fields are highly correlated in their research interests. Nevertheless, the two fields serve different academic audiences, have different research foci, and theorise IT artifacts differently. We investigate the dyadic relationship between health information systems (HIS) research in IS and HI through the communication patterns between the two fields. We present the citation analysis results of HIS research published in IS and HI journals between 2000 and 2020. The results revealed that despite the two fields sharing a common interest, communication between them is limited and only about specific topics. Potentially relevant ideas and theories generated in IS have not yet been sufficiently recognised by HI scholars and incorporated into the HI literature. However, the upward trend of HIS publications in IS indicates that IS has the potential to contribute more to HI.

Pay-for-performance programs effectiveness in healthcare: the case of the end-stage renal disease quality incentive program

This paper focuses on Medicare's End-Stage Renal Disease Quality Incentive Program (QIP). QIP aims to promote high-quality services in outpatient dialysis facilities by tying their payments to their performance on pre-specified quality measures. In this paper, employing principal-agent theory, we examine the effectiveness of QIP by exploring the changes in various clinical/operational measures when they become a part of the program as a performance measure. We study five QIP quality measures; two are operational: hospitalization and readmission. And three others are clinical: blood transfusion, hypercalcemia, and dialysis adequacy. Overall, we observe a significant improvement in all QIP quality measures after being included in the program, except for readmission. We recommend adjusting the weight and redesigning the readmission measure for Medicare to incentivize providers to reduce readmission. We also discuss establishing care coordination and employing data-driven clinical decision support systems as opportunities for dialysis facilities to improve the care delivery process.

Blood Demand Forecasting and Supply Management: An Analytical Assessment of Key Studies Utilizing Novel Computational Techniques

Use of data-driven methodologies in enhancing blood transfusion practices is rising, leveraging big data, machine learning, and optimization techniques to improve demand forecasting and supply chain management. This review used a narrative approach to identify, evaluate, and synthesize key studies that considered novel computational techniques for blood demand forecasting and inventory management through a search of PubMed and Web of Sciences databases for studies published from January 01, 2016, to March 30, 2023. The studies were analyzed for their utilization of various techniques, and their strengths, limitations, and areas for improvement. Seven key studies were identified. The studies focused on different blood components using various computational methods, such as regression, machine learning, hybrid models, and time series models, across different locations and time periods. Key variables used for demand forecasting were largely derived from electronic health record data, including clinical related predictors such as laboratory test results and hospital census by location. Each study offered unique strengths and valuable insights into the use of data-driven methods in blood bank management. Common limitations were unknown generalizability to other healthcare settings or blood components, need for field-specific performance measures, lack of ABO compatibility consideration, and ethical challenges in resource allocation. While data-driven research in blood demand forecasting and management has progressed, limitations persist and further exploration is needed. Understanding these innovative, interdisciplinary methods and their complexities can help refine inventory strategies and address healthcare challenges more effectively, leading to more robust, accurate models to enhance blood management across diverse healthcare scenarios.

Which information locations in covered entities under HIPAA must be secured first? A multi-criteria decision-making approach

Creating adequate safeguards for physical and online locations (e.g., desktop computers, network servers) where protected health information (PHI) may be breached is critical for management within entities compliant with the Health Information Portability and Accountability Act (HIPAA). With the increasing complexity of cyber breaches and budgetary issues, prioritizing which locations require the most immediate attention by top management through a data-driven model is more important than ever. Using CORAS threat modeling and five methods for multi-criteria decision-making, these locations were ranked from greatest to least risk of data breaches. Statistical methods were subsequently used for consistency and robustness checks. The findings illustrate that each type of covered entity under HIPAA must prioritize a different set of locations to safeguard first: health care providers must focus on the security of network servers, other portable electronic devices, and category of others (i.e., miscellaneous locations); health plans must focus on the security of paper and films, network servers, and others; and business associates must focus on the security of category of others, network servers, and other portable electronic devices. Combined with data on the source of the breaches (external vs. internal) and type of threats (e.g., hacking, theft), these findings provide recommendations for risk identification for privacy officers across health care.

Pathologists' acceptance of telepathology in the Ministry of National Guard Health Affairs Hospitals in Saudi Arabia: A survey study

Introduction

Evaluating users' attitudes and acceptance of telemedicine in the early phases of implementation is critical in predicting a successful adoption and utilization of the service. Telepathology does not require a patient's presence for intercommunication; therefore, it is essential to focus on the acceptance of the main occupational groups that are involved. This study aimed to assess the attitude and acceptance of telepathology in the pathology departments of the Ministry of National Guard Health Affairs (MNGHA) hospitals in Saudi Arabia.

Method

A cross-sectional survey was distributed among pathologists and laboratory technologists in the pathology departments of MNGHA hospitals (N = 78). The data collection instrument was built upon the technology acceptance model's (TAM's) constructs of perceived usefulness (PU), perceived ease of use (PEU), attitude (ATT), and intention to use (ITU).

Results

In total, 64 questionnaires were completed (response rate of 82%). The acceptance levels represented by the median responses to the TAM constructs, varied from 5.5 (slightly agree) to 6 (agree). The availability of digital pathology services in the workplace was significantly correlated with the participants' acceptance of telepathology. There was a strong correlation between ITU and PU and a moderate correlation between PEU and PU.

Conclusion

Results suggest that telepathology is more likely to be adopted if it is considered helpful, therefore, it is recommended to focus on its usefulness and direct benefits during the training period. The participants who were familiar with the concept of digital pathology were more receptive to using telepathology, which might emphasize the importance of introducing and familiarizing the resident with digital health during their training period.

Using Telemedicine during the COVID-19 Pandemic: How Service Quality Affects Patients' Consultation

The COVID-19 epidemic put pressure on the traditional healthcare system and offline consultation methods. Telemedicine platforms provide a more convenient and safer channel for online health communication. Based on the signaling theory, our study explores the impacts of three dimensions of physicians' service quality (need fulfillment, security, and responsiveness) on online patient consultation on telemedicine platforms. A negative binomial model was used to test cross-sectional data of 2982 physicians obtained from Haodf.com. The results show the following: (1) the need fulfillment dimension variables positively affect online patient consultation; (2) the security dimension variables positively affect online patient consultation; (3) the responsiveness dimension variables positively affect online patient consultation. Our results contribute to the theoretical aspect of signaling theory and service quality in the context of telemedicine platforms and have several practical implications for telemedicine platform physicians and platform operators.

Validation of a computerized decision support system to review pharmacotherapy treatment: scheduling guidelines

BACKGROUND

The review of pharmacotherapy can be conceptualized as a service in which the drugs used by the patient are reviewed to control the risks as well as to improve the results of the drug therapy, detecting, solving, and preventing issues associated with the drug, readjusting the doses and times (schedule) so that the treatment is not incompatible or in duplicity.

METHODS

The aim of the study was to validate an intelligent information system, which was developed to assist the scheduling activity in the pharmacotherapy review. The system used the concept of Genetic Algorithms. To validate the system, hypothetical cases were elaborated considering various aspects of pharmacotherapy such as underdose, overdose, drug interactions and contraindications. These cases were tested in the system and were also analyzed by pharmaceutical experts with clinical and research experience in the pharmacotherapy review process. The degree of agreement between the assessments of the appointments carried out by the pharmaceutical specialists and by the system were measured using the Kappa index with a 95% confidence interval.

RESULTS

In detecting errors and make propositions, the system was able to identify 80% of errors, with pharmaceutical experts identifying between 20 and 70% of errors. In relation the results of kappa between the cases, the system had 87,3% of concordance, whereas the best pharmaceutical expert had 75,5% of concordance, considering the correct answer.

CONCLUSION

It can be concluded that with the methodology used, the investigation met the objectives and confirmed the system is effective for pharmaceutical review process. There are indications that the system can help in the Pharmacotherapy review process, being able to find prescription errors as well as to establish times for the use of medications according to the patient's routine.

Understanding the Antecedents of the Routine Use of Mobile Health Services: A Person-Technology-Health Framework

Although numerous studies have been conducted to understand the antecedents of usage of mobile health (mHealth) services, most of them solely focus on characteristics of mHealth services themselves but neglect taking users' psychological and health-related factors into consideration. Besides, the comprehensive understanding of what influences users' routine use intentions regarding mHealth services is lacking. Therefore, this study proposes a person-technology-health framework that underlines how personal factors (e.g., personal innovativeness in IT), technological factors (e.g., trust), and health factors (e.g., perceived health severity) jointly influence individuals' routine use intentions regarding mHealth services. The proposed research model and related hypotheses were tested based on survey data from 270 respondents. The results indicate that personal innovativeness in IT, trust, and perceived health severity are important for enhancing routine use intention of mHealth services. Specifically, in situations of high perceived health severity, trust relates less positively to routine use intention than personal innovativeness in IT. In contrast, in situations of low perceived health severity, trust relates more positively to routine use intention than personal innovativeness in IT. The research findings extend the existing literature on routine use intention related to mHealth services and provide significant implications for practitioners.

Identity Threats as a Reason for Resistance to Artificial Intelligence: Survey Study With Medical Students and Professionals

Background

Information systems based on artificial intelligence (AI) have increasingly spurred controversies among medical professionals as they start to outperform medical experts in tasks that previously required complex human reasoning. Prior research in other contexts has shown that such a technological disruption can result in professional identity threats and provoke negative attitudes and resistance to using technology. However, little is known about how AI systems evoke professional identity threats in medical professionals and under which conditions they actually provoke negative attitudes and resistance.

Objective

The aim of this study is to investigate how medical professionals’ resistance to AI can be understood because of professional identity threats and temporal perceptions of AI systems. It examines the following two dimensions of medical professional identity threat: threats to physicians’ expert status (professional recognition) and threats to physicians’ role as an autonomous care provider (professional capabilities). This paper assesses whether these professional identity threats predict resistance to AI systems and change in importance under the conditions of varying professional experience and varying perceived temporal relevance of AI systems.

Methods

We conducted 2 web-based surveys with 164 medical students and 42 experienced physicians across different specialties. The participants were provided with a vignette of a general medical AI system. We measured the experienced identity threats, resistance attitudes, and perceived temporal distance of AI. In a subsample, we collected additional data on the perceived identity enhancement to gain a better understanding of how the participants perceived the upcoming technological change as beyond a mere threat. Qualitative data were coded in a content analysis. Quantitative data were analyzed in regression analyses.

Results

Both threats to professional recognition and threats to professional capabilities contributed to perceived self-threat and resistance to AI. Self-threat was negatively associated with resistance. Threats to professional capabilities directly affected resistance to AI, whereas the effect of threats to professional recognition was fully mediated through self-threat. Medical students experienced stronger identity threats and resistance to AI than medical professionals. The temporal distance of AI changed the importance of professional identity threats. If AI systems were perceived as relevant only in the distant future, the effect of threats to professional capabilities was weaker, whereas the effect of threats to professional recognition was stronger. The effect of threats remained robust after including perceived identity enhancement. The results show that the distinct dimensions of medical professional identity are affected by the upcoming technological change through AI.

Conclusions

Our findings demonstrate that AI systems can be perceived as a threat to medical professional identity. Both threats to professional recognition and threats to professional capabilities contribute to resistance attitudes toward AI and need to be considered in the implementation of AI systems in clinical practice.

How IT Investments Help Hospitals Gain and Sustain Reputation in the Media: The Role of Signaling and Framing

Practice- and Policy-Oriented Abstract

Understanding how IT investments help organizations to build and sustain reputation is of particular relevance for healthcare practitioners and policy makers because patients are often unable to assess the quality of care, relying instead on the reputation of health service providers in the media, such as newspapers. As information intermediaries, journalists detect, aggregate, and translate the weaker signals for quality, such as state-of-the-art IT, that a hospital emanates. Our analysis of 152 hospital organizations in England, complemented by interviews with healthcare journalists, shows that journalists write less negatively about hospitals when healthcare organizations’ IT equipment investments are high. This implies that investments in IT equipment can buffer hospitals from negative press, thereby helping them to gain and maintain a strong reputation in the media. Practitioners and policy makers may incorporate the reputational effect of IT when making investment decisions and further amplify such IT investment through press releases, corporate reports, and media interactions.

Impacts of physician promotion on the online healthcare community: Using a difference-in-difference approach

In this study, we use a difference-in-difference approach to explore how physician promotion, the advancement of a physician's offline reputation, affects patient behavior toward physicians in online healthcare communities; this allows us to explore how patients interpret the signals created by physician promotion. The study sample was collected from over 140,000 physician online profiles after 25 months of continuous observation, with 280 physicians who were promoted at month 13 as the treatment group and a control group obtained by propensity score matching. Our results show that a physician's promotion causes more patients to choose that physician, makes patients willing to give more psychological rewards, and makes them tend to give that physician a higher online rating. This implies that patient behavior is susceptible to the signal of physician promotion because the quality of the physician is unlikely to have changed significantly in the short term. These findings extend prior research on reputation in online communities and have crucial implications for theory and practice.

Knowledge Sharing Among Healthcare Practitioners: Identifying the Psychological and Motivational Facilitating Factors

There exists a lack of an understanding of how to facilitate knowledge sharing (KS) behaviors in healthcare organizations. This study is among the first to specifically address this issue through synthesizing psychological ownership (PO), self-determination theory, and psychological empowerment (PE) theory. This study developed a research model that described the impact of the psychological and motivational facilitating factors, including autonomous motivation, user PE, and PO on knowledge sharing intention (KSI) and knowledge sharing behavior (KSB). Data collected from 343 healthcare professionals were analyzed using the technique of partial least squares (PLS) to validate the research model. The results indicated that user PE, organization-based PO, and autonomous motivation all had significant direct/indirect positive effects on KSI and KSB as we hypothesized. Surprisingly, knowledge-based PO had a significant positive effect on KSI, which contradicted our original hypothesis. The implications for theory and for practice, limitations, and future research directions are discussed accordingly.

Ethical Attitudes toward COVID-19 Passports: Evidences from Spain

A so-called COVID-19 passport or Immunity passport (IP) has been proposed to facilitate the mobility of individuals while the SARS-CoV-2 pandemic persists. A COVID-19 passport can play a key role in the control of the pandemic, specifically in areas with a high density of population, and the help of smart city technology could be very useful to successfully implement IPs. This research studies the impact of ethical judgments on user attitudes toward using vaccine passports based on a Multidimensional Ethics Scale (MES) that contains five ethical constructs: moral equity, relativism, egoism, utilitarianism, and contractualism. Regression analysis shows that MES satisfactorily explains attitude (R² = 87.82%, p < 0.001) and that a positive evaluation in moral equity, egoism and utilitarianism is significant (p < 0.001). The objective of the passport (variable leisure) shows a significant negative moderating effect on moral equity (coefficient = −0.147, p = 0.0302) and a positive one on relativism (coefficient = 0.158, p = 0.0287). Adjustment by means of fsQCA shows that five ethical constructs satisfactorily explain both favorable and unfavorable attitudes toward IPs. Solutions explaining acceptance attain an overall consistency (cons) = 0.871 and coverage (cov) = 0.980. In the case of resistance, we found that cons = 0.979 and cov = 0.775. However, that influence is asymmetrical. To have a positive attitude toward the passport, it is a sufficient condition to attain a positive evaluation on a single ethical factor. On the other hand, when explaining resistance, and with the exception of the recipe ~utilitarianism (cons = 0.911 and cov = 0.859), explanatory prime implications require the interaction of at least two variables. Likewise, the context in which the passport is required is significant to explain rejection.

Contradictions and Interventions in Health IS

The study analyzes data collected in two case studies in the healthcare industry, which is characterized by a variety of social and technical elements forming an activity system where all elements interact with each other. The findings indicate that many problems emerging during the implementation of a health information system can be traced back to contradictions between elements of the activity systems that are created or amplified by the new IS. The authors find that some contradictions are latent and become salient when introducing a new IS, while other contradictions are (unintentionally) newly created. Also, the study shows that contradictions are more complex than hitherto assumed and often concern more than two elements of a healthcare activity system. In a similar vein, effective interventions geared toward countering these contradictions are found to account for additional complexity while not always achieving their goal. Drawing on activity theory, the authors develop a framework to coherently synthesize the findings. The study can help increase the understanding of the IS’s role within an activity system and help guide IS implementation projects aimed at avoiding unintended consequences.

Online User Information Sharing and Government Pandemic Prevention and Control Strategies-Based on Evolutionary Game Model

Background: The sharing and utilization of online users' information has become an important resource for governments to manage COVID-19; however, it also involves the risk of leakage of users' personal information. Online users' sharing decisions regarding personal information and the government's COVID-19 prevention and control decisions influence each other and jointly determine the efficiency of COVID-19 control and prevention. Method: Using the evolutionary game models, this paper examines the behavioral patterns of online users and governments with regard to the sharing and disclosure of COVID-19 information for its prevention and control. Results: This paper deduce the reasons and solutions underlying the contradiction between the privacy risks faced by online users in sharing information and COVID-19 prevention and control efforts. The inconsistency between individual and collective rationality is the root cause of the inefficiency of COVID-19 prevention and control. Conclusions: The reconciliation of privacy protection with COVID-19 prevention and control efficiency can be achieved by providing guidance and incentives to modulate internet users' behavioral expectations.

Designing Personalized Treatment Plans for Breast Cancer

Breast cancer remains the leading cause of cancer deaths among women around the world. Contemporary treatment for breast cancer is complex and involves highly specialized medical professionals collaborating in a series of information-intensive processes. This poses significant challenges to optimization of treatment plans for individual patients. We propose a novel framework that enables personalization and customization of treatment plans for early stage breast cancer patients undergoing radiotherapy. Using a series of simulation experiments benchmarked with real-world clinical data, we demonstrate that the treatment plans generated from our proposed framework consistently outperform those from the existing practices in balancing the risk of local tumor recurrence and radiation-induced adverse effects. Our research sheds new light on how to combine domain knowledge and patient data in developing effective decision-support tools for clinical use. Although our research is specifically geared toward radiotherapy planning for breast cancer, the design principles of our framework can be applied to the personalization of treatment plans for patients with other chronic diseases that typically involve complications and comorbidities.

Qualitative exploration of health system response to COVID-19 pandemic applying the WHO health systems framework: Case study of a Nigerian state

Pandemics can result in significantly high rates of morbidity and mortality with higher impact in Lower- and Middle-Income Countries like Nigeria. Health systems have an important role in a multi-sector response to pandemics, as there are already concerns that COVID-19 will significantly divert limited health care resources. This study appraised the readiness and resilience of the Nigerian health system to the COVID-19 pandemic, using Oyo State, southwest Nigeria, as a case study. This study was a cross-sectional qualitative study involving key informant and in-depth interviews. Purposive sampling was used in recruiting participants who were members of the Task Force on COVID-19 in the state and Emergency Operations Centre (EOC) members (physicians, nurses, laboratory scientists, “contact tracers”, logistic managers) and other partners. The state's health system response to COVID 19 was assessed using the WHO health systems framework. Audio recordings of the interviews done in English were transcribed and thematic analysis of these transcripts was carried out using NVIVO software. Results show that the state government responded promptly by putting in place measures to address the COVID-19 pandemic. However, the response was not adequate owing to the fact that the health system has already been weakened by various challenges like poor funding of the health system, shortage of human resources and inadequate infrastructure. These contributed to the health system's sub-optimal response to the pandemic. In order to arm the health system for adequate and appropriate response during major health disasters like pandemics, fundamental pillars of the health system-finance, human resources, information and technology, medical equipment and leadership - need to be addressed in order to have a resilient health system.

The Effects of Log-in Behaviors and Web Reviews on Patient Consultation in Online Health Communities: Longitudinal Study

Background

With the rapid development of information technology and web-based communities, a growing number of patients choose to consult physicians in online health communities (OHCs) for information and treatment. Although extant research has primarily discussed factors that influence the consulting choices of OHC patients, there is still a lack of research on the effects of log-in behaviors and web reviews on patient consultation.

Objective

This study aims to explore the impact of physicians’ log-in behavior and web reviews on patient consultation.

Methods

We conducted a longitudinal study to examine the effects of physicians’ log-in behaviors and web reviews on patient consultation by analyzing short-panel data from 911 physicians over five periods in a Chinese OHC.

Results

The results showed that the physician’s log-in behavior had a positive effect on patient consultation. The maximum number of days with no log-ins for a physician should be 20. The two web signals (log-in behavior and web reviews) had no complementary relationship. Moreover, the offline signal (ie, offline status) has different moderating effects on the two web signals, positively moderating the relationship between web reviews and patient consultation.

Conclusions

Our study contributes to the eHealth literature and advances the understanding of physicians’ web-based behaviors. This study also provides practical implications, showing that physicians’ log-in behavior alone can affect patient consultation rather than complementing web reviews.

Designing Payment Contracts for Healthcare Services to Induce Information Sharing: The Adoption and the Value of Health Information Exchanges (HIEs)

Recent initiatives to improve healthcare quality and reduce costs have centered around payment mechanisms and IT-enabled health information exchanges (HIEs). Such initiatives profoundly influence both providers’ choices in terms of healthcare effort levels and HIE adoption and patients’ choice of providers. Using a game-theoretical model of a healthcare setup, we examine the role of payment models in aligning providers’ and patients’ incentives for realizing socially optimal (i.e., first-best) choices. We show that the traditional fee-for-service (FFS) payment model does not necessarily induce the first-best solution. The pay-for-performance (P4P) model may induce the first-best solution under some conditions if provider switching by patients during a health episode is socially suboptimal, making provider coordination less of an issue. We identify an episode-based payment (EBP) model that can always induce the first-best solution. The proposed EBP model reduces to the P4P model if the P4P model induces the first-best solution. In other cases, the first-best inducing EBP model is multilateral in the sense that the payment to a provider depends not only on the provider’s own efforts and outcomes but also on those of other providers. Furthermore, the payment in this EBP model is sequence dependent in the sense that payment to a provider is contingent upon whether the patient visits a given provider first or second. We show that the proposed EBP model achieves the lowest healthcare cost, not necessarily at the expense of care quality or provider payment, relative to FFS and P4P. Although our proposed contract is complex, it sets an optimality baseline when evaluating simpler contracts and also characterizes aspects of payment that need to be captured for socially desirable actions. We further show that the value of HIEs depends critically on the payment model as well as on the social desirability of patient switching. Under all three payment models, the HIE value is higher when switching by at least some patients is desirable than when switching by any patient is undesirable. Moreover, the HIE value is highest under the FFS model and lowest under the P4P model. Hence, assessing the value of HIEs in isolation from the underlying payment mechanism and patient-switching behavior may result in under- or overestimation of the HIE value. Therefore, as payment models evolve over time, there is a real need to reevaluate the HIE value and the government subsidies that induce providers to adopt HIEs.

Removing barriers for digital health through organizing ambidexterity in hospitals

Aim

Hospitals noticeably struggle with maintaining hundreds of IT systems and applications in compliance with the latest IT standards and regulations. Thus, hospitals search for efficient opportunities to discover and integrate useful digital health innovations into their existing IT landscapes. In addition, although a multitude of digital innovations from digital health startups enter the market, numerous barriers impede their successful implementation and adoption. Against this background, the aim of this study was to explore typical digital innovation barriers in hospitals, and to assess how a hospital data management platform (HDMP) architecture might help hospitals to extract such innovative capabilities.

Subject and methods

Based on the concept of organizational ambidexterity (OA), we pursued a qualitative mixed-methods approach. First, we explored and consolidated innovation barriers through a systematic literature review, interviews with 20 startup representatives, and a focus group interview with a hospital IT team and the CEO of an HDMP provider. Finally, we conducted a case-study analysis of 36 digital health startups to explore and conceptualize the potential impact of DI and apply the morphological method to synthesize our findings from a multi-level perspective.

Results

We first provide a systematic and conceptual overview of typical barriers for digital innovation in hospitals. Hereupon, we explain how an HDMP might enable hospitals to mitigate such barriers and extract value from digital innovations at both individual and organizational level.

Conclusion

Our results imply that an HDMP can help hospitals to approach organizational ambidexterity through integrating and maintaining hundreds of systems and applications, which allows for a structured and controlled integration of external digital innovations.

Implementing an Integrated Large-Scale Clinical Information System for ISSSTE's Hospital Network in Mexico

The Institute for Security and Social Services for State Workers (ISSSTE) is a large public provider of health care services that serve around 13.2 million Mexican government workers and their families. To attain process efficiencies, cost reductions, and improvement of the quality of diagnostic and imaging services, ISSSTE was set out in 2019 to create a digital filmless medical image and report management system. A large-scale clinical information system (CIS), including radiology information system (RIS), picture archiving and communication system (PACS), and clinical data warehouse (CDW) components, was implemented at ISSSTE's network of forty secondary- and tertiary-level public hospitals, applying global HL-7 and Digital Imaging and Communications in Medicine (DICOM) standards. In just 5 months, 40 hospitals had their endoscopy, radiology, and pathology services functionally interconnected within a national CIS and RIS/PACS on secure private local area networks (LANs) and a secure national wide area network (WAN). More than 2 million yearly studies and reports are now in digital form in a CDW, securely stored and always available. Benefits include increased productivity, reduced turnaround times, reduced need for duplicate exams, and reduced costs. Functional IT solutions allow ISSSTE hospitals to leave behind the use of radiographic film and printed medical reports with important cost reductions, as well as social and environmental impacts, leading to direct improvement in the quality of health care services rendered.

Supplementary Information

The online version of this article (10.1007/s42399-020-00713-2) contains supplementary material, which is available to authorized users.

Overcoming Resistance to Big Data and Operational Changes Through Interactive Data Visualization

Research has shown that the use of big data can modify operational processes in organizations. However, little research has been conducted on overcoming resistance to the process changes needed for adoption of big data technologies. In this article, we address this gap in the literature by investigating the impact of interactive data visualization on decision-making around operational process changes with big data. Our goal is to demonstrate how the choice of visualization of workflow and operational processes impacts decisions to embrace real-time, big data technology. To do so, we conduct a case study of patient/provider interactions in a large health care practice and compare the initial state with a revised workflow using a big data, real-time analytics platform. We then investigate the impact of the data visualization strategy on decision-making to implement operational changes caused by big data. The study demonstrates that interactive data visualization of operational processes can be an enabler in overcoming organizational resistance to big data technologies in a change-resistant organization. The concomitant benefit is that big data analytics is placed directly into the hands of primary decision makers.

Going digital: a checklist in preparing for hospital-wide electronic medical record implementation and digital transformation

Objective In an era of rapid digitisation of Australian hospitals, practical guidance is needed in how to successfully implement electronic medical records (EMRs) as both a technical innovation and a major transformative change in clinical care. The aim of the present study was to develop a checklist that clearly and comprehensively defines the steps that best prepare hospitals for EMR implementation and digital transformation. Methods The checklist was developed using a formal methodological framework comprised of: literature reviews of relevant issues; an interactive workshop involving a multidisciplinary group of digital leads from Queensland hospitals; a draft document based on literature and workshop proceedings; and a review and feedback from senior clinical leads. Results The final checklist comprised 19 questions, 13 related to EMR implementation and six to digital transformation. Questions related to the former included organisational considerations (leadership, governance, change leaders, implementation plan), technical considerations (vendor choice, information technology and project management teams, system and hardware alignment with clinician workflows, interoperability with legacy systems) and training (user training, post-go-live contingency plans, roll-out sequence, staff support at point of care). Questions related to digital transformation included cultural considerations (clinically focused vision statement and communication strategy, readiness for change surveys), management of digital disruption syndromes and plans for further improvement in patient care (post-go-live optimisation of digital system, quality and benefit evaluation, ongoing digital innovation). Conclusion This evidence-based, field-tested checklist provides guidance to hospitals planning EMR implementation and separates readiness for EMR from readiness for digital transformation. What is known about the topic? Many hospitals throughout Australia have implemented, or are planning to implement, hospital wide electronic medical records (EMRs) with varying degrees of functionality. Few hospitals have implemented a complete end-to-end digital system with the ability to bring about major transformation in clinical care. Although the many challenges in implementing EMRs have been well documented, they have not been incorporated into an evidence-based, field-tested checklist that can practically assist hospitals in preparing for EMR implementation as both a technical innovation and a vehicle for major digital transformation of care. What does this paper add? This paper outlines a 19-question checklist that was developed using a formal methodological framework comprising literature review of relevant issues, proceedings from an interactive workshop involving a multidisciplinary group of digital leads from hospitals throughout Queensland, including three hospitals undertaking EMR implementation and one hospital with complete end-to-end EMR, and review of a draft checklist by senior clinical leads within a statewide digital healthcare improvement network. The checklist distinguishes between issues pertaining to EMR as a technical innovation and EMR as a vehicle for digital transformation of patient care. What are the implications for practitioners? Successful implementation of a hospital-wide EMR requires senior managers, clinical leads, information technology teams and project management teams to fully address key operational and strategic issues. Using an issues checklist may help prevent any one issue being inadvertently overlooked or underemphasised in the planning and implementation stages, and ensure the EMR is fully adopted and optimally used by clinician users in an ongoing digital transformation of care.

Data-Driven Personalization of a Physiotherapy Care Pathway: Case Study of Posture Scanning

Background

Advanced sensor, measurement, and analytics technologies are enabling entirely new ways to deliver health care. The increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making or even by automating some parts of decision making in relation to the care process.

Objective

The aim of this study was to analyze how digital data acquired from posture scanning can enhance physiotherapy services and enable more personalized delivery of physiotherapy.

Methods

A case study was conducted with a company that designed a posture scan recording system (PSRS), which is an information system that can digitally record, measure, and report human movement for use in physiotherapy. Data were collected through interviews with different stakeholders, such as health care professionals, health care users, and the information system provider, and were analyzed thematically.

Results

Based on the results of our thematic analysis, we propose three different types of support that posture scanning data can provide to enhance and enable more personalized delivery of physiotherapy: 1) modeling the condition, in which the posture scanning data are used to detect and understand the health care user’s condition and the root cause of the possible pain; 2) visualization for shared understanding, in which the posture scanning data are used to provide information to the health care user and involve them in more collaborative decision-making regarding their care; and 3) evaluating the impact of the intervention, in which the posture scanning data are used to evaluate the care progress and impact of the intervention.

Conclusions

The adoption of digital tools in physiotherapy has remained low. Physiotherapy has also lacked digital tools and means to inform and involve the health care user in their care in a person-centered manner. In this study, we gathered insights from different stakeholders to provide understanding of how the availability of digital posture scanning data can enhance and enable personalized physiotherapy services.

More than Words in Medical Question-and-Answer Sites: A Content-Context Congruence Perspective

Application Abstract

Understanding Physicians’ Online-Offline Behavior Dynamics: An Empirical Study

Online healthcare platforms allow physicians and patients to communicate in a timely manner. Yet little is known about how physicians’ online and offline activities affect each other and, consequently, the healthcare system. We collected data from both online and offline channels to study physicians’ online-offline behavior dynamics. We find that physicians’ online activities can lead to a higher service quantity in offline channels, whereas offline activities may reduce physicians’ online services because of resource constraints. We also find that the more offline patients that physicians serve, the more articles the physicians will likely share in online healthcare platforms. These findings are of great importance to practitioners and policy makers. Our work provides evidence that online healthcare platforms supplement offline services and thus lessen the concern that physicians’ participation in online healthcare platforms will negatively influence offline healthcare services. Our findings also indicate the need for the improvement of online-offline coordination and better system design.

People who inject drugs (PWID) and HIV/aids cases in Mexico City: 1987-2015

Background

The purpose of this study is to describe the characteristics of individuals who inject drugs, to explore use trends in the past 25 years, and to review the profile of users of various drugs, both legal and illegal, that have been used intravenously without medical prescription in Mexico City.

Methods

Information was drawn from the Drug Information Reporting System (SRID, 1987–2015) and data from the National Center for the Prevention and Control of HIV/aids (CENSIDA, 1983–2018). SRID is based on two 30-day cross-sectional evaluations carried out during June and November. It has served as an uninterrupted epidemiological surveillance system for 32 years, operating both in health and justice institutions in Mexico City and the metropolitan area. The timely identification of changes in use patterns is regarded as the most consistent tool to track the trajectory of the phenomenon. CENSIDA cases were analyzed based on the number of HIV and aids positive injectable drug users during the same period of time in Mexico City.

Results

Cocaine users represented the highest number of cases, with a total of N = 293. Back in 2000, the use of this substance showed a significant increase of up to 50%. In turn, heroine and opiates user trends by sex increased from being almost non-existent in 1987 to 13% in 1994. Results provide evidence of the changes in the number of users over the years, with the largest increases being recorded in 1996 (16.5%), 1999 (17%), and 2010 (13%).

Conclusions

The increase observed in the results coincides with domestic and world political situations that have caused an upturn in the use of some substances over the years. It is not far-fetched to think that in the coming years there will be an increase in the number of individuals who inject drugs due to the high production and availability of heroin in Mexico and the emergence of fentanyl use as indicated by ethnographic research in Mexico City and the deaths linked to its consumption. The latest reports, published in 2018, documented at least five cases of fentanyl users.

Long story short: finding health advice with informative summaries on health social media

Purpose

Whether automatically generated summaries of health social media can aid users in managing their diseases appropriately is an important question. The purpose of this paper is to introduce a novel text summarization approach for acquiring the most informative summaries from online patient posts accurately and effectively.

Design/methodology/approach

The data set regarding diabetes and HIV posts was, respectively, collected from two online disease forums. The proposed summarizer is based on the graph-based method to generate summaries by considering social network features, text sentiment and sentence features. Representative health-related summaries were identified and summarization performance as well as user judgments were analyzed.

Findings

The findings show that awarding sentences without using all the incorporating features decreases summarization performance compared with the classic summarization method and comparison approaches. The proposed summarizer significantly outperformed the comparison baseline.

Originality/value

This study contributes to the literature on health knowledge management by analyzing patients’ experiences and opinions through the health summarization model. The research additionally develops a new mindset to design abstractive summarization weighting schemes from the health user-generated content.

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

This paper presents a systematic review of the literature and the classification of fuzzy logic application in an infectious disease. Although the emergence of infectious diseases and their subsequent spread have a significant impact on global health and economics, a comprehensive literature evaluation of this topic has yet to be carried out. Thus, the current study encompasses the first systematic, identifiable and comprehensive academic literature evaluation and classification of the fuzzy logic methods in infectious diseases. 40 papers on this topic, which have been published from 2005 to 2019 and related to the human infectious diseases were evaluated and analyzed. The findings of this evaluation clearly show that the fuzzy logic methods are vastly used for diagnosis of diseases such as dengue fever, hepatitis and tuberculosis. The key fuzzy logic methods used for the infectious disease are the fuzzy inference system; the rule-based fuzzy logic, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy cognitive map. Furthermore, the accuracy, sensitivity, specificity and the Receiver Operating Characteristic (ROC) curve were universally applied for a performance evaluation of the fuzzy logic techniques. This thesis will also address the various needs between the different industries, practitioners and researchers to encourage more research regarding the more overlooked areas, and it will conclude with several suggestions for the future infectious disease researches.