Introduction to health information exchange

To support health care and public health in managing the array of information available about patients and populations, health systems have adopted a variety of information and communications technologies (ICT). Examples include electronic health record systems that document patient symptoms, diseases, and medications as well as health care processes. Yet, many ICT systems operate as islands unto themselves, unable to connect or share information with other ICT systems. Such fragmentation of data and information is an impediment to achieving the goal of efficient, coordinated health care delivery. It was further a major challenge during the COVID-19 pandemic when information was rapidly needed yet challenging to access. Health information exchange (HIE) seeks to address the challenges of connecting disparate ICT systems, enabling information to be available when and where it is needed by clinicians, administrators, and public health authorities. This chapter robustly defines HIE, including its core components and various forms. This chapter further discusses the role of HIE in supporting care delivery and public health. © 2023 Elsevier Inc. All rights reserved.

Shared longitudinal health records for clinical and population health

The ability of a health information exchange (HIE) to consolidate information, collected from multiple, disparate information systems, into a single, person-centric health record can provide a comprehensive and longitudinal representation of an individual's medical history. Shared, longitudinal health records can be leveraged to enhance the delivery of individual clinical care and provide opportunities to improve health outcomes at the population level. This chapter describes the clinical benefits imparted by the shared health record (SHR) component an HIE infrastructure. It also characterizes the potential public health benefits of the aggregate level, population health indicators calculated, stored, and distributed by a health management information system (HMIS) component. Tools for visualizing health indicators from the HMIS, including disease surveillance systems developed during the COVID-19 pandemic, are also described. Postpandemic components such as the SHR and HMIS will likely play critical roles in strengthening health information infrastructures in states and nations. © 2023 Elsevier Inc. All rights reserved.

[Digitalization in healthcare: today and in the future]. / Digitalisierung im Gesundheitswesen: heute und in Zukunft.

Although Germany continues to struggle with the digital transformation of healthcare, there is reason for optimism. The political will to improve healthcare with digital technologies has been underpinned by numerous legal initiatives since 2018. In addition, there is growing acceptance among healthcare providers and the population. The latter has clearly been driven by the corona pandemic, which underscored the need for more digitized care.Digitalization in healthcare has three key drivers: the rapid technological development in data processing, the ever-improving understanding of the biological basis of human life, and growing patient sovereignty coupled with a growing desire for transparency. Prerequisites for digital medicine are data interoperability and the establishment of a networking (telematics) infrastructure (TI). The status of the most important digital TI applications affecting German healthcare are described: the electronic patient record (ePA) as its core as well as electronic prescriptions, medication plans, and communication tools such as Communication in Medicine (KIM) and TI Messenger (TIM). In addition, various telemedical offerings are discussed as well as the introduction of digital health applications (DiGA) into the statutory healthcare system, which Germany has pioneered. Furthermore, the use of medical data as the basis for artificial intelligence (AI) algorithms is discussed. While helpful and capable of improving diagnostics as well as medical therapy, such AI tools will not replace doctors and nurses.

Use of Deep Learning Application in Medical Devices

Artificial Intelligence (AI) is a system that helps machines to march with human abilities within daily lifestyles. Deep learning supported by AI can be an effective application within healthcare sector. This research has explained various aspects of Deep learning application that can be a major area of concern for pushing the development process of Indian medical sector that have lack of infrastructure and lack of capacity, to take less time to optimise the medical diagnosis process. This research has also investigated the advantages and disadvantages that medical sector might face while using deep learning applications. Deep learning applications under AI systems are used to classify objects. CNN model, Machine-learning tools, and other tools that use deep learning approach are effective to diagnose any disease and in medical image analysis process. Deep learning techniques are also used to detect heart disease and manage the data regarding the patients of heart diseases. Secondary data collection method has been used and a thematic analysis has been conducted in this research to describe and find various challenges that might have been engaged within deep learning process used in medical sectors of India. It has been found that, Deep Learning is used widely for COVID-19 medical image processing through a fully connected CNN model. As a result, the main finding states that deep learning application creates a major scope for the improvement in Indian medical sector. © 2022 IEEE.

COVID-19 in people with neurofibromatosis 1, neurofibromatosis 2, or schwannomatosis.

PURPOSE: People with pre-existing conditions may be more susceptible to severe COVID-19 when infected by SARS-CoV-2. The relative risk and severity of SARS-CoV-2 infection in people with rare diseases such as neurofibromatosis type 1 (NF1), neurofibromatosis type 2 (NF2), or schwannomatosis (SWN) is unknown. METHODS: We investigated the proportions of people with NF1, NF2, or SWN in the National COVID Cohort Collaborative (N3C) electronic health record data set who had a positive test result for SARS-CoV-2 or COVID-19. RESULTS: The cohort sizes in N3C were 2501 (NF1), 665 (NF2), and 762 (SWN). We compared these with N3C cohorts of patients with other rare diseases (98-9844 individuals) and the general non-NF population of 5.6 million. The site- and age-adjusted proportion of people with NF1, NF2, or SWN who had a positive test result for SARS-CoV-2 or COVID-19 (collectively termed positive cases) was not significantly higher than in individuals without NF or other selected rare diseases. There were no severe outcomes reported in the NF2 or SWN cohorts. The proportion of patients experiencing severe outcomes was no greater for people with NF1 than in cohorts with other rare diseases or the general population. CONCLUSION: Having NF1, NF2, or SWN does not appear to increase the risk of being SARS-CoV-2 positive or of being a patient with COVID-19 or of developing severe complications from SARS-CoV-2.

Inference from longitudinal laboratory tests characterizes temporal evolution of COVID-19-associated coagulopathy (CAC).

Temporal inference from laboratory testing results and triangulation with clinical outcomes extracted from unstructured electronic health record (EHR) provider notes is integral to advancing precision medicine. Here, we studied 246 SARS-CoV-2 PCR-positive (COVIDpos) patients and propensity-matched 2460 SARS-CoV-2 PCR-negative (COVIDneg) patients subjected to around 700,000 lab tests cumulatively across 194 assays. Compared to COVIDneg patients at the time of diagnostic testing, COVIDpos patients tended to have higher plasma fibrinogen levels and lower platelet counts. However, as the infection evolves, COVIDpos patients distinctively show declining fibrinogen, increasing platelet counts, and lower white blood cell counts. Augmented curation of EHRs suggests that only a minority of COVIDpos patients develop thromboembolism, and rarely, disseminated intravascular coagulopathy (DIC), with patients generally not displaying platelet reductions typical of consumptive coagulopathies. These temporal trends provide fine-grained resolution into COVID-19 associated coagulopathy (CAC) and set the stage for personalizing thromboprophylaxis.

Information Streams in Health Facilities: The Case of Uganda

With the prevailing COVID-19 pandemic, the lack of digitally-recorded and connected health data poses a challenge for analysing the situation. Virus outbreaks, such as the current pandemic, allow for the optimisation and reuse of data, which can be beneficial in managing future outbreaks. However, there is a general lack of knowledge about the actual flow of information in health facilities, which is also the case in Uganda. In Uganda, where this case study was conducted, there is no comprehensive knowledge about what type of data is collected or how it is collected along the journey of a patient through a health facility. This study investigates information flows of clinical patient data in health facilities in Uganda. The study found that almost all health facilities in Uganda store patient information in paper files on shelves. Hospitals in Uganda are provided with paper tools, such as reporting forms, registers and manuals, in which district data is collected as aggregate data and submitted in the form of digital reports to the Ministry of Health Resource Center. These reporting forms are not digitised and, thus, not machine-actionable. Hence, it is not easy for health facilities, researchers, and others to find and access patient and research data. It is also not easy to reuse and connect this data with other digital health data worldwide, leading to the incorrect conclusion that there is less health data in Uganda. The a FAIR architecture has the potential to solve such problems and facilitate the transition from paper to digital records in the Uganda health system. © 2022 Chinese Academy of Sciences. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data.

The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital "Istituti Clinici Salvatore Maugeri" in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.

Investigating the Role of Using Electronic Health Record (EHR) in Physician-Patient Relationship: A Qualitative Study

Introduction: Electronic Health Record (EHR) is an opportunity to implement healthcare services remotely especially in situations that social distance is necessary, such as in Covid-19 pandemic. The relationship between physician and patient is very important in medicine and it has been described as a basic axis of clinical measures and the foundation stone of proper activities in the health system. However, one of the main factors less considered in the design and deployment of health technologies is the physician-patient relationship, emotions, and feelings. The present study aimed to investigate the role of using electronic health records on physician-patient relationship. Methods: In this study, qualitative data collection was carried out via unstructured and semi-structured interviews and focus group discussion, using a phenomenological approach. A thematic analysis approach was also used to analyze the transcripts. A total of 24 participants, including eight physicians, three specialists (pediatricians, gynecologists, and psychiatrists), four psychologists, five health care providers, and four chief executive officers were selected using purposive sampling. Results: The results of this study revealed using EHR could influence interpersonal communication as well as empathetic and sympathetic relationship between physician and patient. The relevant classes are explained in detail in the main text of the article. Conclusion: While EHR is beneficial, the concerns overshadowing the interactions between physician and patient cause users to change their perception of the benefits and efficiency of EHR. © 2022, Kerman University of Medical Sciences. All rights reserved.

Training to improve patient-centered electronic health record (EHR) use

Background: Electronic health records (EHRs) are used across healthcare systems to reduce clinical care errors, improve care team communication, and enhance care coordination and patient safety. However, one criticism is that EHRs increase the provider’s engagement with the computer and decrease engagement with the patient leading to less patient-centered care. Patient-centered care is personalized care tailored to individual patient needs and preferences. Interventions have been suggested to help manage EHR use during visits while balancing computer interaction and patient-centered care. Methods: Using the Resident-as-Teacher: a layered learning intervention, patient care training, patient-centric EHR use and team development of residents is balanced with creating a shared understanding of these processes. New interns serve as scribes for the senior residents, observing how patient care is conducted while taking notes necessary for charting and billing requirements. The intern and senior resident together navigate the EHR to ensure proper documentation. In addition, attending physicians precept every patient, providing the aspect of layered learning. The roles are then reversed and the senior resident becomes the intern’s scribe. The intern is able to focus on patient care without any distractions that would have been present if the intern was fully in charge of the visit. The resident addresses any missed items with the intern before conclusion of each office visit. EHR training in this manner resulted in 15% more patient encounters while building rapport between the residents. Our assessment included an examination of patient visit counts and an open-ended survey administered to all interns and residents. Adapting training to telehealth during COVID-19 highlights adaptations to in-person training that could be implemented in the virtual environment while maintaining connection between the preceptor and resident. For example, use of a “virtual precepting room”, providing the most up to date best practice information and training residents how to provide the best possible care with the limited information received when only seeing the patient virtually. We conducted qualitative interviews with residents approximately one month into the training to assess residents’ perceptions of its impact and support they received. Results: Analysis of the Resident-as-Teacher suggests that it provided more patient interactions for interns and residents, as well as facilitated rapport building on the team. For adapting training to telehealth, interviews with residents noted a few challenges but support from attendings was appreciated. Conclusions: EHRs have been viewed more as a system required in health care and less of a tool to aid in organization and communication. With appropriate training, EHRs can be an asset to clinical care while working in conjunction with patient-centered care. Providers working together during a resident training period can promote a beneficial learning curve for both patient care and reducing providers’ amount of work after clinic. © 2022 by the authors.

SLR: A Systematic Literature Review on Blockchain Applications in Healthcare

In a very less time from its advent, blockchain technology has entered into mainstream research all over the world. The Bitcoin cryptocurrency is the first open-source implementation of blockchain technology. The application of blockchain technology is not restricted to financial services only;several other sectors especially healthcare is increasingly focusing their attention to grab potential and unique features of blockchain technology like immutability, security, and trust. This paper aims to review the exploration of blockchain technology in the healthcare domain in recent years and study how these emerging technologies help healthcare to speed up treatment procedures and mitigate the effects of the COVID-19 pandemic. © 2021 IEEE.

Informatics response to address the COVID-19 pandemic in a safety net healthcare system.

In service of particularly vulnerable populations, safety net healthcare systems must nimbly leverage health information technology (IT), including electronic health records (EHRs), to coordinate the medical and public health response to the novel coronavirus (COVID-19). Six months after the San Francisco Department of Public Health implemented a new EHR across its hospitals and citywide clinics, California declared a state of emergency in response to COVID-19. This paper describes how the IT and informatics teams supported San Francisco Department of Public Health's goals of expanding the safety net healthcare system capacity, meeting the needs of specific vulnerable populations, increasing equity in COVID-19 testing access, and expanding public health analytics and research capacity. Key enabling factors included critical partnerships with operational leaders, early identification of priorities, a clear governance structure, agility in the face of rapidly changing circumstances, and a commitment to vulnerable populations.

Improving racial disparities in unmet palliative care needs among intensive care unit family members with a needs-targeted app intervention: The ICUconnect randomized clinical trial.

INTRODUCTION: The technologies used to treat the millions who receive care in intensive care unit (ICUs) each year have steadily advanced. However, the quality of ICU-based communication has remained suboptimal, particularly concerning for Black patients and their family members. Therefore we developed a mobile app intervention for ICU clinicians and family members called ICUconnect that assists with delivering need-based care. OBJECTIVE: To describe the methods and early experiences of a clustered randomized clinical trial (RCT) being conducted to compare ICUconnect vs. usual care. METHODS AND ANALYSIS: The goal of this two-arm, parallel group clustered RCT is to determine the clinical impact of the ICUconnect intervention in improving outcomes overall and for each racial subgroup on reducing racial disparities in core palliative care outcomes over a 3-month follow up period. ICU attending physicians are randomized to either ICUconnect or usual care, with outcomes obtained from family members of ICU patients. The primary outcome is change in unmet palliative care needs measured by the NEST instrument between baseline and 3 days post-randomization. Secondary outcomes include goal concordance of care and interpersonal processes of care at 3 days post-randomization; length of stay; as well as symptoms of depression, anxiety, and post-traumatic stress disorder at 3 months post-randomization. We will use hierarchical linear models to compare outcomes between the ICUconnect and usual care arms within all participants and assess for differential intervention effects in Blacks and Whites by adding a patient-race interaction term. We hypothesize that both compared to usual care as well as among Blacks compared to Whites, ICUconnect will reduce unmet palliative care needs, psychological distress and healthcare resource utilization while improving goal concordance and interpersonal processes of care. In this manuscript, we also describe steps taken to adapt the ICUconnect intervention to the COVID-19 pandemic healthcare setting. ENROLLMENT STATUS: A total of 36 (90%) of 40 ICU physicians have been randomized and 83 (52%) of 160 patient-family dyads have been enrolled to date. Enrollment will continue until the end of 2021.