Healthcare Information Management Research Articles

Artificial intelligence in healthcare facilities asset information management: mixed review

Healthcare facilities are pivotal in ensuring continuous access to services, particularly for individuals with complex health conditions. Effective asset information management (AIM) in these facilities through artificial intelligence (AI) can enhance operational efficiency. The exploratory bibliometric and systematic review assesses the status and trends of AI applications in AIM within healthcare facilities. The findings reveal a significant gap between research findings and practical implementation, highlighting the need for further integration and real-world usage of AI-powered solutions in healthcare facilities settings. This study identified notable gaps, including the need for research on utilising AI to enhance asset management in healthcare, including maintenance scheduling and procurement processes. Involving stakeholders, such as healthcare professionals, facility managers, and patients, in effective facility management using AI requires further investigation. Research is needed to evaluate the economic benefits and develop robust ethical guidelines for responsible AI implementation. Notably, previous research has given limited attention to AI for healthcare AIM, with emerging trends focusing more on AI and infrastructure than the ‘asset’ aspect. Implementing AI-powered solutions tailored to the unique needs of healthcare facilities and evaluating cost-effectiveness will lead to improved asset management practices, enhanced decision-making processes, and, ultimately, more efficient and effective healthcare operations.

HARNESSING BIG DATA AND MACHINE LEARNING FOR TRANSFORMATIVE HEALTHCARE INFORMATION MANAGEMENT

This study explores the transformative impact of big data and machine learning (ML) on healthcare information management, drawing insights from a comprehensive review of 50 peer-reviewed articles published between 2010 and 2023. The analysis highlights significant advancements in predictive analytics, with ML models improving disease prediction accuracy by up to 40% compared to traditional methods, as demonstrated in multiple studies. Personalized medicine has also benefited, with ML-driven treatment plans showing a 25% improvement in chronic disease management and a 15% reduction in treatment failures, as reported in several case studies. Additionally, integrating big data and ML into healthcare operations has led to a 35% reduction in patient wait times and a 20% decrease in hospital readmissions. However, challenges such as data fragmentation, privacy concerns, and algorithmic bias were identified in over 60% of the reviewed studies, indicating the need for stronger regulatory frameworks. This review underscores the potential of big data and ML to revolutionize healthcare while also calling attention to the ethical and practical challenges that must be addressed to ensure equitable and transparent healthcare solutions.

Tiramisù: making sense of multi-faceted process information through time and space

AbstractKnowledge-intensive processes represent a particularly challenging scenario for process mining. The flexibility that such processes allow constitutes a hurdle as they are hard to capture in a single model. To tackle this problem, multiple visual representations of the same processes could be beneficial, each addressing different information dimensions according to the specific needs and background knowledge of the concrete process workers and stakeholders. In this paper, we propose, describe, and evaluate a framework, named , that leverages visual analytics for the interactive visualization of multi-faceted process information, aimed at supporting the investigation and insight generation of users in their process analysis tasks. is based on a multi-layer visualization methodology that includes a visual backdrop that provides context and an arbitrary number of superimposed and on-demand dimension layers. This arrangement allows our framework to display process information from different perspectives and to project this information onto a domain-friendly representation of the context in which the process unfolds. We provide an in-depth description of the approach’s founding principles, deeply rooted in visualization research, that justify our design choices for the whole framework. We demonstrate the feasibility of the framework through its application in two use-case scenarios in the context of healthcare and personal information management. Plus, we conducted qualitative evaluations with potential end users of both scenarios, gathering precious insights about the efficacy and applicability of our framework to various application domains.

Factors Influencing SIMRS Quality at Al-Islam H.M Mawardi General Hospital

This quantitative study investigates the impact of organizational factors on Hospital Management Information Systems (HMIS), employing the PRISM framework. The absence of a search menu linked to the primary key of national identity number in the HMIS was scrutinized for its effects. Results reveal significant influences of organizational factors on technical and behavioral aspects of HMIS. Notably, periodic evaluations by the IT team significantly enhance technological advancements within hospitals, while frequent training sessions for HMIS users markedly improve their comprehension of system functionalities. These findings underscore the pivotal role of organizational strategies in shaping the effectiveness and efficiency of HMIS, offering valuable insights for optimizing healthcare information management practices. Highlights : Organizational factors significantly influence technical enhancements in Hospital Management Information Systems. Frequent evaluations by the IT team contribute to technological advancements within hospital systems. Regular user training sessions enhance comprehension and utilization of HMIS functionalities Keywords: Hospital Management Information Systems, Organizational Factors, PRISM Analysis, Technology Improvement, User Training

Enhancing Patient Consent Management through Blockchain Technology: A Promising Approach for Healthcare Data Security

In contemporary healthcare systems, ensuring patient consent management while safeguarding data privacy and security is a paramount concern. Traditional approaches to managing patient consent often face challenges related to security vulnerabilities, data breaches, and lack of transparency. This research paper proposes a novel approach to address these challenges by leveraging blockchain technology for patient consent management. Blockchain, with its decentralized and immutable ledger, offers a promising solution for securely recording and managing patient consent in healthcare settings. This paper presents a detailed exploration of the concept, design, and implementation of a blockchain-based patient consent management system. Through a comprehensive literature review, methodology description, and evaluation of the proposed system, this research demonstrates the potential benefits of blockchain technology in enhancing patient privacy and data security. The findings of this study contribute to the advancement of healthcare data management practices and provide valuable insights for future research and development in this evolving field. Keywords— Patient consent management, Blockchain technology, Healthcare data security, Privacy protection, Decentralization, Immutable ledger, Transparency, System architecture, Data integrity, Healthcare information management

Reasons for the Success or Failure of a Hospital Information System: A Systematic Review

Objectives: Information and communication technologies have significantly improved the quality of healthcare information management and operations. Hospital information systems (HIS) have become popular and used by most healthcare facilities, both public and private hospitals and clinics. For the best implementation of the health information system, it is necessary to know the reasons leading to failure or success. Objectives is to determine the main reasons for success and failure through a systematic review to help underdeveloped countries like Morocco, which is starting to implement the HIS. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was adhered during the conduct of this systematic review. This systematic review was based on the PubMed database, the Google Scholar search engine Scopus, and science Direct over a considered period of 10 years from 2013 to June 2023. We have included articles dealing with either success factors or failure factors following the implementation of a hospital information system that were published between 2013 and 2023. Results: 28 studies were selected to highlight 8 factors likely to cause success, the most important of which were organizational and behavioral factors containing sub-factors, and 6 factors likely to cause HIS failure, the most important of which was the technical factor. Conclusion: These factors must be considered by the personnel in charge of implementing new information systems to increase the chances of success and to minimize the reasons that can lead to the failure of these systems.

Modeling the enablers of blockchain technology implementation for information management in healthcare supply chains

PurposeBlockchain technology enhances information management in healthcare supply chains by securing healthcare information and providing medical resource traceability. However, there is no decision framework to support blockchain implementation for managing information, especially in emerging economies’ healthcare supply chains. This paper develops a hierarchical decision model for implementing blockchain technology for information management in emerging economies’ healthcare supply chains.Design/methodology/approachThis study uses 20 health supply chain experts in Ghana to rank 17 decision criteria for implementing blockchain for healthcare information management using the best-worst method (BWM) multi-criteria decision technique.FindingsThe results show that “security” and “privacy,” “infrastructural facility” and “presence of training facilities” are the top three critical factors impacting blockchain adoption in the health supply chain for healthcare information management. Other sub-factors are prioritized.Practical implicationsTo implement blockchain effectively to enhance information management in the healthcare supply chain, health institutions, blockchain technology providers and state authorities should concentrate on the highly critical factors extracted from the study.Originality/valueThis is the first study that develops a hierarchical decision model for implementing blockchain technology in emerging economies' health supply chains.

Using electronic health records to improve healthcare information management

Using electronic health records to improve healthcare information management

Using electronic health records to improve healthcare information management

Using electronic health records to improve healthcare information management

A holistic insight into the privacy & security of cloud-based computing approach on healthcare information management systems in the United States – a grounded theory approach

A holistic insight into the privacy & security of cloud-based computing approach on healthcare information management systems in the United States – a grounded theory approach

Assessment of clinical miscoding errors and potential financial their implications on healthcare management – A case of local hospital in Najran, Saudi Arabia

BackgroundHealthcare systems can potentially improve their safety, quality of service, and performance efficiency with a cost reduction, through the introduction and implementation of healthcare information management systems. This study aims to examine the frequency of miscoding errors in principal and secondary diagnoses, exploring demographic and coder-related factors contributing to these errors through the use of the QuadraMed system. The study also investigates the association of coding errors with patient safety and service quality to estimate the potential financial implications resulting from these inaccuracies in the healthcare system. MethodsThis analytical cross-sectional retrospective study was conducted at a local hospital in Najran, Saudi Arabia, from July 2021 to February 2022 using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) coding system. The costing and financial data were collected from the reimbursement department for eligible 750 patient cases in terms of payment mode, services availed, and length of stay. The financial claims were evaluated to estimate the impact on the quality of service and patient safety. The reimbursement amount was calculated based on codes. The data were analyzed using SPSS and the odds ratio was calculated to estimate the risk of major coding errors in different departments. ResultsPrimary codes 240 (32%) and 40 (5.3) secondary codes were reviewed and percentages and inaccuracies were calculated after recording. The percentage of inaccurate medical codes in principal diagnosis was 57(26.8%) and the percentage of inaccurate medical codes in secondary diagnosis was 21 (9.9%). The primary diagnostic codes have more coding errors with a total number of 240 (32%) coding errors with a moderate level of agreement between the original coder and independent coder with a kappa value of 0.462. The identified recording was done by the independent coder, and the secondary diagnostic code showed 40 (5.3%) cases, with a poor kappa value of 0.128. The results showed the highest number of primary diagnostic codes was among surgery clinics 79 (63.2%). The highest number of secondary diagnostic codes were reported among consultant clinics 12 (9.6%) ConclusionsThe study concludes that the identification of miscoding in the healthy population has a financial impact on the healthcare organization's infrastructure.

Human Error and Clinical Data Sharing

Computer security issues have a widespread impact across all sectors and are increasingly influencing the healthcare industry. Since 2019, several reported attacks have been specifically aimed at exploiting vulnerabilities in medical network protocols like 'DICOM' and 'HL7,' as well as file formats, particularly those related to digital imaging and medical communications. While numerous articles delve into subjects such as security and blockchain, they often disregard the significant role that human errors play in compromising security. Human errors within hospital computer systems can lead to substantial implications for both patient care and data security. This article delves into the various forms of human errors that can occur within such systems and assesses their potential consequences on the overall effectiveness and security of healthcare information management. The study underscores the necessity of effective mitigation strategies to reduce the associated risks of these errors and enhance the dependability of hospital computer systems

Healthcare information management and operational cost performance: empirical evidence.

Healthcare knowledge management systems can mitigate hospitals' operational inefficiency. As a healthcare information technology, the electronic health record (EHR) receives much attention from medical institutions due to its considerable impact on operational cost performance. This paper focuses on EHR systems to address operational inefficiency by which patients pay more for health care services, and many U.S. hospitals are filing for bankruptcy. From the theoretical perspective of the practice-based view, this paper introduces a path to implement EHR systems for improving cost performance. The empirical investigation is archival data of 200 hospitals collected from the U.S. healthcare agencies. Findings contribute to prior work by hypothesizing moderating and mediating roles in EHR systems implementation. This paper introduces absorptive capacity and monitoring mechanisms as enablers of implementing EHR systems. The results showed that hospital monitoring strengthens the relationship between absorptive capacity and electronic health record systems implementation, which results in better operational cost performance. Theoretically, this study supports the long-term potential benefits of EHR adoption, and its findings are consistent with optimizing efficiency through data standardization and interoperability. From a practical perspective, this study supports hospitals' investments in evolving healthcare information technology systems through the development of a knowledge-based system employing EHR, particularly when hospitals are merging or need a financial strategic plan to control expenses.

Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration.

Generative artificial intelligence (AI) and large language models (LLMs), exemplified by ChatGPT, are promising for revolutionizing data and information management in healthcare and medicine. However, there is scant literature guiding their integration for non-AI professionals. This study conducts a scoping literature review to address the critical need for guidance on integrating generative AI and LLMs into healthcare and medical practices. It elucidates the distinct mechanisms underpinning these technologies, such as Reinforcement Learning from Human Feedback (RLFH), including few-shot learning and chain-of-thought reasoning, which differentiates them from traditional, rule-based AI systems. It requires an inclusive, collaborative co-design process that engages all pertinent stakeholders, including clinicians and consumers, to achieve these benefits. Although global research is examining both opportunities and challenges, including ethical and legal dimensions, LLMs offer promising advancements in healthcare by enhancing data management, information retrieval, and decision-making processes. Continued innovation in data acquisition, model fine-tuning, prompt strategy development, evaluation, and system implementation is imperative for realizing the full potential of these technologies. Organizations should proactively engage with these technologies to improve healthcare quality, safety, and efficiency, adhering to ethical and legal guidelines for responsible application.

The burden of scoliosis: a nationwide database study on demographics, incidence, and surgical rates.

Scoliosis is characterized as a three-dimensional deformity of the spine, affected by variety of factors, including biological, mechanical, hormonal, and genetics. Our study's primary objective was to delineate the demographics, incidence, and prevalence of scoliosis from a nationwide perspective, analyze the surgical intervention rates, with the aim of offering more insightful guidance to orthopedic physicians. This nationwide cohort study was conducted from digital database for healthcare information management. Relevant population data, for children under 18 years old, was extracted from the official government census within the period of January 2015 to December 2022. Utilizing diagnostic code of M41 [Scoliosis] was used to define the patient pool from ICD-10. Demographic variables, type of operations (posterior only, anterior only, or combined), and complications were documented. Incidence and prevalence values were calculated using population figures and case numbers. There were 276,521 patients with an average incidence of 129 per 100,000. Frequency of females was 1.45 times greater than that of males (p < 0.001). A total of 10,417 surgeries were performed in 10,311 patients during the inspection period (3.8% of all cases). Posterior fusion was by far the most common surgical approach (n = 10.111; 97%) followed by anterior fusion (n = 200; 1,9%). Our findings reveal a significant increase in the average incidence of scoliosis diagnosis, rising from 107 per 100,000 individuals in 2015 to 161 per 100,000 in 2022. Scoliosis now impacts an estimated 1.2% of children and adolescents in Turkey. The risk is 1.45 times higher in females than in males.

The minimum data set of electronic personal health records for Alzheimer's disease using design science methodology

ObjectiveLong-term management of healthcare information for chronic patients such as Alzheimer's is complex and tedious. An electronic Personal Health Record (ePHR) can help patients by maintaining a history of healthcare data from various centers. The purposes of this research were to develop a minimum data set (MDS) to design an ePHR for caregivers of patients with Alzheimer's disease (AD) using design science methodology and to identify the necessary measures for a smooth ePHR implementation in Iran. MethodsThis study was performed in Kerman from February 2020 to October 2021. To inform caregivers about ePHRs, information sessions were held and a pictorial method was used. Then, 20 semi-structured interviews were conducted with seven physicians and 13 caregivers involved in the care of Alzheimer's patients. Using thematic analysis based on the Information System Research (ISR) framework, the data were encoded and analyzed. ResultsThe MDS of an ePHR for AD contained 178 data elements, categorized into six main sections. The participants considered the use of an ePHR essential to achieve quality care. Using an ePHR can serve as the main method of collecting accurate histories of each Alzheimer's patient in different disease stages and monitoring their behavioral changes. Data confidentiality and security were considered as one of the main measures for AD ePHR implementation. ConclusionThis study identified an AD-specific MDS, as the first major step in launching ePHRs for Alzheimer's patients in the future. The MDS was developed based on the requirements of neurologists and caregivers.

Bluetooth-Based Healthcare Information and Medical Resource Management System.

This paper presents a healthcare information and medical resource management platform utilizing wearable devices, physiological sensors, and an indoor positioning system (IPS). This platform provides medical healthcare information management based on the physiological information collected by wearable devices and Bluetooth data collectors. The Internet of Things (IoT) is constructed for this medical care purpose. The collected data are classified and used to monitor the status of patients in real time with a Secure MQTT mechanism. The measured physiological signals are also used for developing an IPS. When the patient is out of the safety zone, the IPS will send an alert message instantly by pushing the server to remind the caretaker, easing the caretaker's burden and offering extra protection for the patient. The presented system also provides medical resource management with the help of IPS. The medical equipment and devices can be tracked by IPS to tackle some equipment rental problems, such as lost and found. A platform for the medical staff work coordination information exchange and transmission is also developed to expedite the maintenance of medical equipment, providing the shared medical information to healthcare and management staff in a timely and transparent manner. The presented system in this paper will finally reduce the loading of medical staff during the COVID-19 pandemic period.

Blockchain innovation for consent self-management in health information exchanges

With the increasing digitalization of health data, patients need to make informed decisions about the online sharing of their protected health information. This necessitates a robust technical infrastructure that enables patients to self-manage consent and the trusted exchange of this information across sharing entities. Unfortunately, current health information exchange systems in the U.S. are limited in both these regards. While there is recent work on digital patient consent management, there is limited work that provides effective solutions for patient self-management of consent. Further, interoperability issues in the way health information exchanges are currently architected and differences in regulations across localities exacerbate the challenges of consent management. In this research, we survey potential patients' willingness to self-manage healthcare-related consent. Having established the desire for consent self-management, we propose a solution that enables the seamless sharing of patient consent across different healthcare providers and health information exchanges. Specifically, we use a rigorous design science approach to create a blockchain-based, self-managed patient consent system and we evaluate the design through an instantiated prototype. The results of our study should be useful to researchers in healthcare information management as well as to practitioners designing consent management systems. Our research contributes to design science research with an innovative, rigorously evaluated, design principles-based artifact that addresses a critical problem of sharing protected health information.

DESIGN AND EVALUATION OF AN IOTA-BASED MEDICAL INFORMATION SYSTEM

The traditional medical information systems are plagued by issues such as data breaches, lack of privacy, and data integrity concerns. This paper presents the design and evaluation of an IOTA-based medical information system aimed at addressing these challenges. In recent years, blockchain technology has emerged as a powerful tool for securing and managing data in a decentralized manner. One area where this technology has the potential to revolutionize the way we do things is in e-medicine. E-medicine, or electronic medicine, refers to the use of technology to deliver healthcare services remotely. This includes telemedicine, online consultations, and remote monitoring of patients' health status. IOTA blockchain technology, in particular, has a lot of potential in e-medicine. IOTA is a distributed ledger technology that uses a directed acyclic graph (DAG) instead of a traditional blockchain. The main ad-vantage of this approach is that it eliminates the need for miners and makes the system more scalable, fast, and energy-efficient. IOTA is also designed to be feeless, making it an ideal choice for microtransactions. In e-medicine, IOTA can be used in several ways. One potential use case is for secure and decentralized storage of patients' medical records. Medical records are highly sensitive and contain confidential information that needs to be protected from unauthorized access. By using IOTA's tamper-proof and immutable ledger, patients can have more control over their medical records and choose who has access to them. This can be especially useful in situations where patients need to share their medical records with multiple healthcare providers or research institutions. By leveraging the unique features of IOTA, such as its feeless microtransactions, scalability, and distributed ledger technology, the proposed system enhances security, privacy, and interoperability in healthcare information management. The evaluation of the system involves performance tests, and a comparison with existing solutions.

Design Theory for Societal Digital Transformation: The Case of Digital Global Health

With societal challenges, including but not limited to human development, equity, social justice, and climate change, societal-level digital transformation (SDT) is of imminent relevance and theoretical interest. While building on local-level efforts, societal-level transformation is a nonlinear extension of the local level. Unfortunately, academic discourse on digital transformation has largely left SDT unaccounted for. Drawing on more than 25 years of intensive, interventionist research engagement with the digital transformation of public healthcare information management and delivery in more than 80 countries in the Global South, we contribute to theorizing SDT in the form of a design theory consisting of six interconnected design principles. These design principles articulate the interplay and tensions of accommodating over time increased diversity and flexibility in digital solutions, while simultaneously connecting local, national, and regional/ global efforts.