The IHI Rochester Report 2022 on Healthcare Informatics Research: Resuming After the CoViD-19.

In 2020, the CoViD-19 pandemic spread worldwide in an unexpected way and suddenly modified many life issues, including social habits, social relationships, teaching modalities, and more. Such changes were also observable in many different healthcare and medical contexts. Moreover, the CoViD-19 pandemic acted as a stress test for many research endeavors, and revealed some limitations, especially in contexts where research results had an immediate impact on the social and healthcare habits of millions of people. As a result, the research community is called to perform a deep analysis of the steps already taken, and to re-think steps for the near and far future to capitalize on the lessons learned due to the pandemic. In this direction, on June 09th-11th, 2022, a group of twelve healthcare informatics researchers met in Rochester, MN, USA. This meeting was initiated by the Institute for Healthcare Informatics-IHI, and hosted by the Mayo Clinic. The goal of the meeting was to discuss and propose a research agenda for biomedical and health informatics for the next decade, in light of the changes and the lessons learned from the CoViD-19 pandemic. This article reports the main topics discussed and the conclusions reached. The intended readers of this paper, besides the biomedical and health informatics research community, are all those stakeholders in academia, industry, and government, who could benefit from the new research findings in biomedical and health informatics research. Indeed, research directions and social and policy implications are the main focus of the research agenda we propose, according to three levels: the care of individuals, the healthcare system view, and the population view.

Clinical Information Systems and Artificial Intelligence: Recent Research Trends.

OBJECTIVES: This survey aims at reviewing the literature related to Clinical Information Systems (CIS), Hospital Information Systems (HIS), Electronic Health Record (EHR) systems, and how collected data can be analyzed by Artificial Intelligence (AI) techniques. METHODS: We selected the major journals (11 journals) collecting papers (more than 7,000) over the last five years from the top members of the research community, and read and analyzed the papers (more than 200) covering the topics. Then, we completed the analysis using search engines to also include papers from major conferences over the same five years. RESULTS: We defined a taxonomy of major features and research areas of CIS, HIS, EHR systems. We also defined a taxonomy for the use of Artificial Intelligence (AI) techniques on healthcare data. In the light of these taxonomies, we report on the most relevant papers from the literature. CONCLUSIONS: We highlighted some major research directions and issues which seem to be promising and to need further investigations over a medium- or long-term period.

DEDICATE: proposal for a conceptual framework to develop dementia-friendly integrated eCare support.

BACKGROUND: Evidence shows that the implementation of information and communication technologies (ICT) enabled services supporting integrated dementia care represents an opportunity that faces multi-pronged challenges. First, the provision of dementia support is fragmented and often inappropriate. Second, available ICT solutions in this field do not address the full spectrum of support needs arising across an individual's whole dementia journey. Current solutions fail to harness the potential of available validated e-health services, such as telehealth and telecare, for the purposes of dementia care. Third, there is a lack of understanding of how viable business models in this field can operate. The field comprises both professional and non-professional players that interact and have roles to play in ensuring that useful technologies are developed, implemented and used. METHODS: Starting from a literature review, including relevant pilot projects for ICT-based dementia care, we define the major requirements of a system able to overcome the limitations evidenced in the literature, and how this system should be integrated in the socio-technical ecosystem characterizing this disease. From here, we define the DEDICATE architecture of such a system, and the conceptual framework mapping the architecture over the requirements. RESULTS: We identified three macro-requirements, namely the need to overcome: deficient technology innovation, deficient service process innovation, and deficient business models innovation. The proposed architecture is a three level architecture in which the center (data layer) includes patients' and informal caregivers' preferences, memories, and other personal data relevant to sustain the dementia journey, is connected through a middleware (service layer), which guarantees core IT services and integration, to dedicated applications (application layer) to sustain dementia care (formal support services, FSS), and to existing formal care infrastructures, in order to guarantee care coordination (care coordination services, CCS). CONCLUSIONS: The proposed DEDICATE architecture and framework envisages a feasible means to overcome the present barriers by: (1) developing and integrating technologies that can follow the patient and the caregivers throughout the development of the condition, since the early stages in which the patient is able to build up preferences and memories will be used in the later stages to maximise personalization and thereby improve efficacy and usability (technology innovation); (2) guaranteeing the care coordination between formal and informal caregivers, and giving an active yet supported role to the latter (service innovation); and (3) integrating existing infrastructures and care models to decrease the cost of the overall care pathway, by improving system interoperability (business model innovation).

Leukocyte Tracking Database, a collection of immune cell tracks from intravital 2-photon microscopy videos.

Recent advances in intravital video microscopy have allowed the visualization of leukocyte behavior in vivo, revealing unprecedented spatiotemporal dynamics of immune cell interaction. However, state-of-the-art software and methods for automatically measuring cell migration exhibit limitations in tracking the position of leukocytes over time. Challenges arise both from the complex migration patterns of these cells and from the experimental artifacts introduced during image acquisition. Additionally, the development of novel tracking tools is hampered by the lack of a sound ground truth for algorithm validation and benchmarking. Therefore, the objective of this work was to create a database, namely LTDB, with a significant number of manually tracked leukocytes. Broad experimental conditions, sites of imaging, types of immune cells and challenging case studies were included to foster the development of robust computer vision techniques for imaging-based immunological research. Lastly, LTDB represents a step towards the unravelling of biological mechanisms by video data mining in systems biology.

Telemedicine for Developing Countries. A Survey and Some Design Issues.

BACKGROUND: Developing countries need telemedicine applications that help in many situations, when physicians are a small number with respect to the population, when specialized physicians are not available, when patients and physicians in rural villages need assistance in the delivery of health care. Moreover, the requirements of telemedicine applications for developing countries are somewhat more demanding than for developed countries. Indeed, further social, organizational, and technical aspects need to be considered for successful telemedicine applications in developing countries. OBJECTIVE: We consider all the major projects in telemedicine, devoted to developing countries, as described by the proper scientific literature. On the basis of such literature, we want to define a specific taxonomy that allows a proper classification and a fast overview of telemedicine projects in developing countries. Moreover, by considering both the literature and some recent direct experiences, we want to complete such overview by discussing some design issues to be taken into consideration when developing telemedicine software systems. METHODS: We considered and reviewed the major conferences and journals in depth, and looked for reports on the telemedicine projects. RESULTS: We provide the reader with a survey of the main projects and systems, from which we derived a taxonomy of features of telemedicine systems for developing countries. We also propose and discuss some classification criteria for design issues, based on the lessons learned in this research area. CONCLUSIONS: We highlight some challenges and recommendations to be considered when designing a telemedicine system for developing countries.

Domains of Health IT and Tailoring of Evaluation: Practicing Process Modeling for Multi-Stakeholder Benefits.

This contribution focuses on the heterogeneity and complexity of health information technology services and systems in a multi-stakeholder environment. We propose the perspective of process modeling as a method to break out complexity, represent heterogeneity, and provide tailored evaluation and optimization of health IT systems and services. Two case studies are presented to show how process modeling is needed to fully understand the information flow, thus identifying requirements and specifications for information system re-engineering and interoperability; detect process weaknesses thus designing corrective measures; define metrics as a mean to evaluate and ensure system quality; and optimize the use of resources.

A Design Methodology for Medical Processes.

BACKGROUND: Healthcare processes, especially those belonging to the clinical domain, are acknowledged as complex and characterized by the dynamic nature of the diagnosis, the variability of the decisions made by experts driven by their experiences, the local constraints, the patient's needs, the uncertainty of the patient's response, and the indeterminacy of patient's compliance to treatment. Also, the multiple actors involved in patient's care need clear and transparent communication to ensure care coordination. OBJECTIVES: In this paper, we propose a methodology to model healthcare processes in order to break out complexity and provide transparency. METHODS: The model is grounded on a set of requirements that make the healthcare domain unique with respect to other knowledge domains. The modeling methodology is based on three main phases: the study of the environmental context, the conceptual modeling, and the logical modeling. RESULTS: The proposed methodology was validated by applying it to the case study of the rehabilitation process of stroke patients in the specific setting of a specialized rehabilitation center. The resulting model was used to define the specifications of a software artifact for the digital administration and collection of assessment tests that was also implemented. CONCLUSIONS: Despite being only an example, our case study showed the ability of process modeling to answer the actual needs in healthcare practices. Independently from the medical domain in which the modeling effort is done, the proposed methodology is useful to create high-quality models, and to detect and take into account relevant and tricky situations that can occur during process execution.

Adherence to Selective Serotonin and Serotonin-Norepinephrine Reuptake Inhibitor Prescriptions Affects Overall Medication Adherence in Older Persons: Evidence From the Italian Nationwide OsMed Health-DB Database.

OBJECTIVE: This study aimed to evaluate prevalence of prescription of and adherence to selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) and whether adherence to these classes of drugs affects overall medication adherence in older persons. METHODS: In a cross-sectional analysis of administrative data comprehensive of all prescribed drugs reimbursed by the Italian national health care system, new prescriptions of SSRIs and SNRIs to persons aged 65 years or older were analyzed (n = 380,400 in 2011; 395,806 in 2012; 409,741 in 2013, from a total sample of 3,762,299 persons aged 65 years or older) as well as prescriptions of antihypertensives, statins, other psychiatric drugs, antidiabetics, antiplatelets, anticoagulants, drugs for chronic obstructive pulmonary disease, and antiosteoporotics. Adherence was estimated by calculating the proportion of days covered by drugs dispensed during a period of 365 days. Adherence was defined as a proportion of days covered of more than 80%. RESULTS: Prevalence of SSRI and SNRI prescriptions varied from 11.4% in 2011 to 12.1% in 2013. Adherence to SSRI and SNRI prescriptions ranged from 31.2% in persons aged ≥ 95 years in 2011 to 41.8% in persons aged 75-84 years in 2013. Persons adherent to SSRI and SNRI prescriptions were more likely to be adherent to the other medications, after adjustment for age, gender, and number of drugs prescribed. The highest association was found for adherence to psychiatric drugs (OR = 1.9; 95% CI, 1.8-2.0). CONCLUSIONS: Adherence to SSRI and SNRI prescriptions is poor in older persons. However, people adherent to these classes of antidepressants are more likely to be adherent to the other medications they are prescribed. Studies are needed to evaluate the reasons for and the potential benefits of increasing adherence to antidepressants on overall adherence.

Advanced Age and Medication Prescription: More Years, Less Medications? A Nationwide Report From the Italian Medicines Agency.

BACKGROUND: In older adults co-occurrence of multiple diseases often leads to use of multiple medications (polypharmacy). The aim of the present study is to describe how prescription of medications varies across age groups, with specific focus on the oldest old. METHODS: We performed a cross-sectional study using 2013 data from the OsMed Health-DB database (mean number of medicines and defined daily doses prescribed in 15,931,642 individuals). There were 3,378,725 individuals age 65 years or older (21.2% of the study sample). RESULTS: The mean number of prescribed medications progressively rose from 1.9 in the age group <65 years to 7.4 in the age group 80-84 years and then declined, with a more marked reduction in the age group 95 years or older with a mean number of 2.8 medications. A similar pattern was observed for the mean number of defined daily doses. Among participants age ≥65 years, proton pump inhibitors were the most commonly prescribed medication (40.9% of individuals ≥65 years), followed by platelet aggregation inhibitors (32.8%) and hydroxy-methylglutaryl-coenzyme A reductase inhibitors (26.1%). A decline in prescription was observed among individuals age 90 years or older, but this reduction was less consistent for medications used to treat acute conditions (ie, antibiotics and glucocorticoids) rather than preventive medicines commonly used to treat chronic diseases (ie, antihypertensive medications and hydroxy-methylglutaryl-coenzyme A reductase inhibitors). CONCLUSIONS: The burden of medication treatment progressively increases till age 85 and substantially declines after age of 90 years. Patterns of medication prescription widely vary across age groups.

Mining approximate temporal functional dependencies with pure temporal grouping in clinical databases.

Functional dependencies (FDs) typically represent associations over facts stored by a database, such as "patients with the same symptom get the same therapy." In more recent years, some extensions have been introduced to represent both temporal constraints (temporal functional dependencies - TFDs), as "for any given month, patients with the same symptom must have the same therapy, but their therapy may change from one month to the next one," and approximate properties (approximate functional dependencies - AFDs), as "patients with the same symptomgenerallyhave the same therapy." An AFD holds most of the facts stored by the database, enabling some data to deviate from the defined property: the percentage of data which violate the given property is user-defined. According to this scenario, in this paper we introduce approximate temporal functional dependencies (ATFDs) and use them to mine clinical data. Specifically, we considered the need for deriving new knowledge from psychiatric and pharmacovigilance data. ATFDs may be defined and measured either on temporal granules (e.g.grouping data by day, week, month, year) or on sliding windows (e.g.a fixed-length time interval which moves over the time axis): in this regard, we propose and discuss some specific and efficient data mining techniques for ATFDs. We also developed two running prototypes and showed the feasibility of our proposal by mining two real-world clinical data sets. The clinical interest of the dependencies derived considering the psychiatry and pharmacovigilance domains confirms the soundness and the usefulness of the proposed techniques.

Querying temporal clinical databases on granular trends.

This paper focuses on the identification of temporal trends involving different granularities in clinical databases, where data are temporal in nature: for example, while follow-up visit data are usually stored at the granularity of working days, queries on these data could require to consider trends either at the granularity of months ("find patients who had an increase of systolic blood pressure within a single month") or at the granularity of weeks ("find patients who had steady states of diastolic blood pressure for more than 3 weeks"). Representing and reasoning properly on temporal clinical data at different granularities are important both to guarantee the efficacy and the quality of care processes and to detect emergency situations. Temporal sequences of data acquired during a care process provide a significant source of information not only to search for a particular value or an event at a specific time, but also to detect some clinically-relevant patterns for temporal data. We propose a general framework for the description and management of temporal trends by considering specific temporal features with respect to the chosen time granularity. Temporal aspects of data are considered within temporal relational databases, first formally by using a temporal extension of the relational calculus, and then by showing how to map these relational expressions to plain SQL queries. Throughout the paper we consider the clinical domain of hemodialysis, where several parameters are periodically sampled during every session.

Temporal representation and reasoning in medicine: Research directions and challenges.

OBJECTIVE: The main aim of this paper is to propose and discuss promising directions of research in the field of temporal representation and reasoning in medicine, taking into account the recent scientific literature and challenging issues of current interest as viewed from the different research perspectives of the authors of the paper. BACKGROUND: Temporal representation and reasoning in medicine is a well-known field of research in the medical as well as computer science community. It encompasses several topics, such as summarizing data from temporal clinical databases, reasoning on temporal clinical data for therapeutic assessments, and modeling uncertainty in clinical knowledge and data. It is also related to several medical tasks, such as monitoring intensive care patients, providing treatments for chronic patients, as well as planning and scheduling clinical routine activities within complex healthcare organizations. METHODOLOGY: The authors jointly identified significant research areas based on their importance as for temporal representation and reasoning issues; the subjects were considered to be promising topics of future activity. Every subject was addressed in detail by one or two authors and then discussed with the entire team to achieve a consensus about future fields of research. RESULTS: We identified and focused on four research areas, namely (i) fuzzy logic, time, and medicine, (ii) temporal reasoning and data mining, (iii) health information systems, business processes, and time, and (iv) temporal clinical databases. For every area, we first highlighted a few basic notions that would permit any reader--including those who are unfamiliar with the topic--to understand the main goals. We then discuss interesting and promising directions of research, taking into account the recent literature and underlining the yet unresolved medical/clinical issues that deserve further scientific investigation. The considered research areas are by no means disjointed, because they share common theoretical and methodological features. Moreover, subjects of imminent interest in medicine are represented in many of the fields considered. CONCLUSIONS: We propose and discuss promising subjects of future research that deserve investigation to develop software systems that will properly manage the multifaceted temporal aspects of information and knowledge encountered by physicians during their clinical work. As the subjects of research have resulted from merging the different perspectives of the authors involved in this study, we hope the paper will succeed in stimulating discussion and multidisciplinary work in the described fields of research.