Database on the coverage of the "Bolsa-Família" conditioning cash-transfer program: Brazil, 2005 to 2021

Objectives

The “Bolsa-Família” Program (PBF) is a Brazilian conditional cash-transfer program in which families should comply with health, education, and social assistance conditionalities. The program aims to fight poverty and hunger, promoting nutrition and health services for low-income populations. This paper presents a database on the coverage of monitoring and compliance with the PBF health conditionalities in Brazil from January 2005 to July 2021.

Data description

Database on the PBF conditioning cash-transfer program coverage in Brazil from 2005 to 2021. It comprises information on the number of families benefited, health conditionalities, and the follow-up on vaccination and nutrition of children under seven years old. The cities and semesters are the minimal aggregation units.

Research Development Using REDCap Software

OBJECTIVES

High-quality clinical research is dependent on adequate design, methodology, and data collection. The utilization of electronic data capture (EDC) systems is recommended to optimize research data through proper management. This paper's objective is to present the procedures of REDCap (Research Electronic Data Capture), which supports research development, and to promote the utilization of this software among the scientific community.

METHODS

REDCap's web application version 10.4.1 released on 2021 (Vanderbilt University) is an EDC system suitable for clinical research development. This paper describes how to join the REDCap consortium and presents how to develop survey instruments and use them to collect and analyze data.

RESULTS

Since REDCap is a web application that stimulates knowledge-sharing among the scientific community, its development is not finished and it is constantly receiving updates to improve the system. REDCap's tools provide access control, audit trails, and data security to the research team.

CONCLUSIONS

REDCap is a web application that can facilitate clinical research development, mainly in health fields, and reduce the costs of conducting research. Its tools allow researchers to make the best use of EDC components, such as data storage.

Developing A General Framework for National Health Information Network for Developing Countries

Background

The National Health Information Network (NHIN) is one of the key issues in health information systems in any country. However, the development of this network should be based on an appropriate framework. Unfortunately, the conducted projects of health information systems in the Ministry of Health of Iran do not fully comply with the concept of NHIN. The present study was aimed to develop a general framework for NHIN in Iran.

Materials and Methods

In this study, in the first stage, the required information about the concept of the NHIN framework and related NHIN documents in the USA and the UK were collected based on a literature review. Then, according to the results of the first stage and with regards to the structure of the Iranian health system, a general framework for Iranian NHIN was proposed. The Delphi technique was conducted to verify the framework.

Results

The proposed framework for Iranian NHIN includes three dimensions; components, principles, and architecture. Over 80% of experts have evaluated all three aspects of the framework at an acceptable scale. In total, the proposed framework has been evaluated by 83.8% of the experts at an acceptable scale.

Conclusion

The proposed framework was expected to serve as the starting point for moving towards the design and creation of Iranian NHIN. At any rate, the framework could be criticized, and it could only be used for the countries whose health system is similar to the structure of the health system in Iran.

Comparison of Knowledge Databases to Be Used in Automated Monitoring of Orthopedic Medical Devices

Surveillance and traceability of medical devices (MD) is a challenge in health care systems. In the perspective of reusing EHR data to automate the monitoring of medical devices, we carried out a comparison of the main MD knowledge bases (MD-KDB) currently available in France. Four MD-KDBs (ANSM, Gudid, Exhausmed and CIOdm) were compared quantitatively and through an example of a shoulder prosthesis. The number of MDs registered differs from one MD-KDB to another. Domain terminologies used in MD-KDBs differ in terms of granularity and in the ease of querying. Waiting EUDAMED, the European MD-KDB, it seems necessary so far to use and combine information coming from several MD-KDBs to address MD monitoring.

The Process of Modeling Information System Architectures with IHE

IHE has defined more than 200 integration profiles in order to improve the interoperability of application systems in healthcare. These profiles describe how standards should be used in particular use cases. These profiles are very helpful but their correct use is challenging, if the user is not familiar to the specifications. Therefore, inexperienced modelers of information systems quickly lose track of existing IHE profiles. In addition, the users of these profiles are often not aware of rules that are defined within these profiles and of dependencies that exist between the profiles. There are also modelers that do not notice the differences between the implemented actors, because they do not know the optional capabilities of some actors. The aim of this paper is therefore to describe a concept how modelers of information systems can be supported in the selection and use of IHE profiles and how this concept was prototypically implemented in the "Three-layer Graph-based meta model" modeling tool (3LGM2 Tool). The described modeling process consists of the following steps that can be looped: defining the use case, choosing suitable integration profiles, choosing actors and their options and assigning them to application systems, checking for required actor groupings and modeling transactions. Most of these steps were implemented in the 3LGM2 Tool. Further implementation effort and evaluation of our approach by inexperienced users is needed. But after that our tool should be a valuable tool for modelers planning healthcare information system architectures, in particular those based on IHE.

Health Data Privacy in the COVID-19 Pandemic Context: Discourses on HIPAA

BACKGROUND

Considering the impacts of the COVID-19 pandemic on health service delivery, the US Office for Civil Rights (OCR) updated the policies on health data processing, and Health Insurance Portability and Accountability Act (HIPAA).

OBJECTIVES

In this study, we investigated discourses on HIPAA in relation to COVID-19.

METHODS

Through a search of media sources in the Factiva database, relevant texts were identified. We applied a text mining approach to identify concepts and themes in these texts.

RESULTS

Our analysis revealed six central themes, namely, Health, HIPAA, Privacy, Security, Patients, and Need, as well as their associated concepts. Among these, Health was the most frequently discussed theme. It comprised concepts such as public, care, emergency, providers, telehealth, entity, use, discretion, OCR, Health and Human Services (HHS), enforcement, business, and services.

CONCLUSION

Our discourse analysis of media outlets highlights the role of health data privacy law in the response to global public health emergencies and demonstrates how discourse analysis and computational methods can inform health data protection policymaking in the digital health era.

Barriers in Health Information Systems and Technologies to Support Maternal and Neonatal Referrals at Primary Health Centers

Objectives

This study explored health workers’ perceptions and experiences regarding the maternal and neonatal referral system, focusing on barriers of health information systems and technologies (IS/IT) at primary health centers (PHCs) in South Tangerang, Indonesia.

Methods

This qualitative study was conducted using semi-structured interviews, which were conducted at the South Tangerang District Health Office and three PHCs. Interviews were conducted with nine participants responsible for maternal and neonatal referrals in their organizations. The data were analyzed using qualitative content analysis.

Results

In South Tangerang, the Primary Health Care Information System (SIMPUS) is used to register patients and record data on medical treatment. To facilitate referrals, the PHCs currently use the Integrated Referral Information System (SISRUTE), P-Care, and the Integrated Emergency Management System (SPGDT). The following four IS/IT barriers to support maternal and neonatal referrals were found: technology, human resources, organizational support, and the referral process and implementation.

Conclusions

The barriers in technology, human resources, and organizational support cause problems in the maternal and neonatal referral process and in the implementation of referrals. Barriers to referrals can influence patients’ ability to receive appropriate care in a timely fashion and lead to inefficiency in maternal and neonatal referrals. This study contributes knowledge about IS/IT implementation in maternal and neonatal referral systems and provides recommendations to health regulators and application developers for the implementation of IS/IT in Indonesia.

Future and Development Direction of Digital Healthcare

OBJECTIVES

Digital healthcare is expected to play a pivotal role in patient-centered healthcare. It empowers patients by informing, communicating, and motivating them. However, a pragmatic evaluation of the present status of digital healthcare has not been presented; therefore, we aimed to examine the status of digital healthcare in Korea.

METHODS

This article discusses digital healthcare, examples of assessment in Korea and other countries, the implications of past examples, and future directions for development.

RESULTS

Over the years, various clinical studies have used clinical evidence to assess the feasibility of digital healthcare. If feasible, it is actually clinically effective. If it is effective, can it be commercialized at an acceptable cost? These questions have been investigated in various evidence-based studies. In addition, great efforts are being made to secure ample evidence to assess various aspects of digital healthcare, such as safety, quality, end-user experience, and equity.

CONCLUSIONS

Digital healthcare requires a deep understanding of both the technical and medical aspects. To strengthen the competence of the medical aspect, medical staff, patients, and the government must work together with continuous interest in this goal.

Maternal and neonatal data collection systems in low- and middle-income countries for maternal vaccines active safety surveillance systems: A scoping review

BACKGROUND

Most post-licensure vaccine pharmacovigilance in low- and middle-income countries (LMICs) are passive reporting systems. These have limited utility for maternal immunization pharmacovigilance in LMIC settings and need to be supplemented with active surveillance. Our study's main objective was to identify existing perinatal data collection systems in LMICs that collect individual information on maternal and neonatal health outcomes and could be developed to inform active safety surveillance of novel vaccines for use during pregnancy.

METHODS

A scoping review was performed following the Arksey and O'Malley six-stage approach. We included studies describing electronic or mixed paper-electronic data collection systems in LMICs, including research networks, electronic medical records, and custom software platforms for health information systems. Medline PubMed, EMBASE, Global Health, Cochrane Library, LILACS, Bibliography of Asian Studies (BAS), and CINAHL were searched through August 2019. We also searched grey literature including through Google and websites of existing relevant perinatal data collection systems, as well as contacted authors of key studies and experts in the field to validate the information and identify additional sources of relevant unpublished information.

RESULTS

A total of 11,817 records were identified. The full texts of 264 records describing 96 data collection systems were assessed for eligibility. Eight perinatal data collection systems met our inclusion criteria: Global Network's Maternal Newborn Health Registry, International Network for the Demographic Evaluation of Populations and their Health; Perinatal Informatic System; Pregnancy Exposure Registry & Birth Defects Surveillance; SmartCare; Open Medical Record System; Open Smart Register Platform and District Health Information Software 2. These selected systems were qualitatively characterized according to seven different domains: governance; system design; system management; data management; data sources, outcomes and data quality.

CONCLUSION

This review provides a list of active maternal and neonatal data collection systems in LMICs and their characteristics as well as their outreach, strengths, and limitations. Findings could potentially help further understand where to obtain population-based high-quality information on outcomes to inform the conduct of maternal immunization active vaccine safety surveillance activities and research in LMICs.

Assessing the potential use of blockchain technology to improve the sharing of public health data in a western Canadian province

This exploratory, qualitative study set out to identify the encountered and perceived barriers to public health (PH) data sharing in a Canadian province with a view to assessing blockchain technology as a potential solution. A topic guide was developed, based on previous research in the area. This was then utilised for ten in-depth, semi-structured interviews with PH professionals between 27 May and 18 June 2019. Each stage of research was congruent with the philosophical underpinning of Gadamerian hermeneutic phenomenology. The major themes that emerged from the data collected were related to the information systems in use, data quality and ownership, as well as client identity management. The recurring core theme throughout all interviews was related to ineffective leadership and management, contributing to each major theme. Overwhelmingly the results show that the majority of barriers faced in this province are human-related. It is concluded that while blockchain technology shows promise for enhancing data sharing in healthcare, it is still many years away from being implemented in this Canadian province. As the results of this study indicate, there are human related barriers that could be addressed in the meantime, which are outside the scope of a technical solution. Future work should explore the perspectives of other stakeholders, such as the provincial government to fully understand the potential for using blockchain to share PH data in this province.

[A New Paradigm in Health Research: FAIR Data (Findable, Accessible, Interoperable, Reusable)]

The digital era, that we are living nowadays, is transforming health, health care models and services, and the role of society in this new reality. We currently have a large amount of stored health data, including clinical, biometric, and scientific research data. Nonetheless, its potential is not being fully exploited. It is essential to foster the sharing and reuse of this data not only in research but also towards the development of health technologies in order to improve health care efficiency, as well as products, services or digital health apps, to promote preventive and individualized medicine and to empower citizens in health literacy and self-management. In this sense, the FAIR concept has emerged, which implies that health data is findable, accessible, shared and reusable, facilitating interoperability between systems, ensuring the protection of personal and sensitive data. In this paper we review the FAIR concept, 'FAIRification' process, FAIR data versus open access data, ethical issues and the general data protection regulation, and digital health and citizen science.

Effects of User Participation in the Development of Health Information Systems on Their Evaluation Within Occupational Health Services

Information management and the usability of health information systems (HIS) are important for the development of HIS in occupational health services. User participation in the HIS development process has been shown to contribute to the success of an HIS. The purpose of this study was to analyze how user participation in HIS development affected evaluation of the success of HIS. The success was assessed on the basis of the DeLone and McLean Information Systems (IS) Success Model. The study was conducted within occupational health services and the data (n=210) was analyzed with quantitative methods. The results showed that users participating in the HIS development process assessed the success of the HIS as better than those that had not taken part in the development. This difference could be seen in all seven dimensions of the DeLone and McLean IS success model but was statistically significant only for System Quality and Intention to Use. The results also showed that the users that had participated in the HIS development process also used the HIS more often and more extensively than those that had not participated in the development. The results indicate that user participation in the development process positively influences their assessment of the HIS and increases their active use of the IS. However, more research is needed to determine the long-term effects of using participatory design in HIS development.

Real-Time Monitoring System to Manage Mental Healthcare Emergency Unit

Objectives

Real-time relevant information helps guide the healthcare decision-making process in daily clinical practice as well as the management and optimization of healthcare processes. However, proprietary business intelligence suite solutions supporting the production of decision-making information requires investment that is out of reach of small and medium-sized healthcare facilities or those with limited resources, particularly in developing countries. This paper describes our experience in designing and implementing a real-time healthcare monitoring system solution to manage healthcare emergency units.

Methods

Through the use of free Business Intelligence tools and Python data science language we designed a real-time monitoring system, which was implemented to explore the Electronic Medical Records system of a university mental health emergency unit and render an electronic dashboard to support health professional daily practice.

Results

Three main dashboards were created to monitor patient waiting time, to access the clinical notes summary for the next waiting patient, and to obtain insights into activity during the last 24 hours.

Conclusions

The designed system could serve as a monitoring support model using free and user-friendly data science tools, which are good alternatives to proprietary business intelligence solutions and drastically reduce cost. Still, the key to success in decision-making systems is based on investment in human resources, business intelligence skills training, the organizational aspect of the decision-making process, and data production quality insurance.

Coping with Information Technology Downtime in Hospitals: A Scoping Study

Downtime of information systems is a universal challenge faced by health care institutions. Regardless of whether downtime is planned or unplanned, the unavailability of essential information requires alternative solutions. In this paper, we conduct a scoping review of how hospitals deal with downtime. A total of 13 papers were included in the final analysis, and we found that coping can be grouped into three strategies; 1) Increasing communication, 2) Analog fallback, and 3) Restricted redundant systems. As the majority of coping mechanisms are related to increasing communication and analog fall back, our findings point to the importance of customizing coping mechanisms for individual healthcare institutions.

Monitoring Mental Healthcare Services Using Business Analytics

Objectives

Monitoring healthcare activities is the first step for health stakeholders and health professionals to improve the quality and performance of healthcare services. However, monitoring remains a challenge for healthcare facilities, especially in developing countries. Fortunately, advances in business analytics address this need. This paper aims to describe the experience of a low-income healthcare facility in a developing country in using business analytics descriptive techniques and to discuss business analytics implementation challenges and opportunities in such an environment.

Methods

Business analytics descriptive techniques were applied on 3 years’ electronic medical records of outpatient consultation of the University Psychiatric Centre (CPU) of Casablanca. Statistical analysis was conducted to compare results over years.

Results

Over the 3 monitored years, the monthly number of computerized physician order entries increased significantly (p < 0.001). Physicians improved their personal recording over years. Schizophrenia as well as depressive and bipolar disorders were noted at the top of outpatient mental disorders. Antipsychotics are the most prescribed drugs, and a significant annual decrease in outpatient care wait time was noted (p < 0.001).

Conclusions

Business analytics allowed CPU to monitor mental healthcare outpatient activity and to adopt its business processes according to outcomes. However, challenges mainly in the organizational dimension of the decision-making process and the definition of strategic key metrics, data structuration, and the quality of data entry had to be considered for the optimal use of business analytics.

Coping with IT Downtime in Hospitals

Downtime of information systems is a universal challenge faced by health care institutions. In this poster, we present the findings of a scoping review of how hospital organizations and their staff deal with downtime, and how coping can be grouped into three strategies; 1) Increasing communication, 2) Analog fallback, and 3) Restricted redundant systems. Our findings point to the importance of customizing coping mechanisms for individual healthcare institutions, and designing systems that empower users to deal with downtime.

Towards Precise Descriptions of Medical Free/Libre and Open Source Software

The web portal Medfloss.org lists over 360 medical free/libre and open source software (MEDFLOSS) projects. These projects are described with the help of a self-developed nomenclature. Due to inconsistencies, the nomenclature shall be replaced by HITO, the Health IT Ontology. HITO is developed iteratively based on different use cases. This paper aims to describe methods and results of the second HITO use case in which HITO is extended to improve the description, retrieval and comparisons of MEDFLOSS projects on Medfloss.org. We use a mixed-methods approach to add concepts and relationships to describe MEDFLOSS precisely. The resulting HITO version stresses functional descriptions based on features and supported enterprise functions, rather than just describing technical characteristics. However, describing a larger number of MEDFLOSS projects requires the commitment of the community.

Qualitative and Quantitative Analysis of Definitions of e-Health and m-Health

Objectives

Skills to employ nursing informatics to promote the health of individuals is of such importance that it is considered a core competence. Although investments are made to increase the use of e-health, there is no full understanding of the usability of e-health for healthcare. This paper presents a current picture of how e-health and m-health are defined and used as well as the effects their usage may have on the intended target group.

Methods

Peer-reviewed open-access papers and grey literature that define e-health and m-health from PubMed, SpringerLink, and Google.com were randomized. A mixed method design with an inductive approach was employed. Open-source software were used for analysis.

Results

The overview includes 30 definitions of e-health and m-health, respectively. The definitions were thematised into 14 narrative themes. The results of the study, and primarily a three-level model, provide an understanding of how different types of e-health and m-health can be put into practice, and the effects or consequences of using them, which may be either positive or negative.

Conclusions

Mobility and flexibility is important for both m-health and e-health. Five keywords that characterize the definitions of e-health and m-health are "health", "mobile", "use", "information", and "technology". E-health or m-health cannot replace human actors because e-health and m-health consist of social and material interactions. Using e-health and m-health is, thus, about developing healthcare without compromising native relics.

Web-Based Spine Segmentation Using Deep Learning in Computed Tomography Images

Objectives

Back pain, especially lower back pain, is experienced in 60% to 80% of adults at some points during their lives. Various studies have found that lower back pain is a very common problem among adolescents, and the highest incidence rates are for adults in their 30s. There has been a remarkable increase in using computer-aided diagnosis to assist doctors in the interpretation of medical images. Spine segmentation in computed tomography (CT) scans using algorithmic methods allows improved diagnosis of back pain.

Methods

In this study, we developed a web-based automatic spine segmentation method using deep learning and obtained the dice coefficient by comparison with the predicted image. Our method is based on convolutional neural networks for segmentation. More specifically, we train a hierarchical data format file using U-Net architecture and then insert the test data label to perform segmentation. Thus, we obtained more specific and detailed results. A total of 344 CT images were used in the experiment. Of these, 330 were used for learning, and the remaining 14 for testing.

Results

Our method achieved an average dice coefficient of 90.4%, a precision of 96.81%, and an F1-score of 91.64%.

Conclusions

The proposed web-based deep learning approach can be very practical and accurate for spine segmentation as a diagnostic method.

Evaluation of the Integrated Health Information System (IHIS) in Public Hospitals in Cyprus Utilizing the DIPSA Framework

INTRODUCTION

The Ministry of Health of the Republic of Cyprus has implemented an Integrated Health Information System (IHIS) in two hospitals. However, no evaluation of IHIS has been conducted to assess its safety, efficiency and effectiveness. The proper utilization of IHIS is essential for the provision of quality healthcare services.

AIM

The purpose of this study was to evaluate the current IHIS in public hospitals in Cyprus utilizing the DIPSA evaluation framework.

METHODS

A total of 309 subjects, including doctors, nurses and other healthcare professionals, participated in the study. The DIPSA evaluation framework assessed the users' perception in five categories namely, satisfaction, collaboration, system quality, safety and procedures, using Likert scale and 3 open questions. Correlation between the categories was assessed using the Pearson correlation coefficient, and multiple regression analysis was used to examine the relationship between the demographic characteristics and categories. Data analysis was done using SPSS v24.

RESULTS

All five categories were rated moderately, between 2.5 and 3, by the participants. All categories were correlated (P < 0.01). Multiple regression analysis indicated the need for improvement between the professionals (mainly doctors and nurses) and the categories. The open questions pointed out the need for improvement in all 3 factors examined (Technology, Human Factor, Organization).

DISCUSSION

The moderately rated categories, in the Cyprus IHIS, suggest that there is a lot of room for improvement. Some interventions are suggested that could positively and simultaneously affect one or more categories.

Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings

PURPOSE

Primary care physicians are increasingly participating in accountable care organizations (ACOs). While prior studies have identified ACO and patient characteristics associated with savings, none have examined characteristics of the communities served by ACOs. Our objective was to assess the relationship between an ACO's service area characteristics and its savings rate.

METHODS

In this cross-sectional study, we used the Centers for Medicare and Medicaid Services 2014 Medicare Shared Savings Program ACO Provider and Beneficiary, and Public Use Files to identify ACO and beneficiary characteristics. We used the American Community Survey to measure community deprivation at the ACO service area-level by using the social deprivation index. The outcome of interest was the ACO savings rate. We conducted bivariate analyses and regressions, adjusting for ACO organization and beneficiary characteristics.

RESULTS

Our sample consisted of 320 ACOs participating in the Shared Savings Plan. The savings rate for ACOs serving the most deprived communities was 1.19% compared with 1.14% for those serving the least deprived. Adjusting for ACO and beneficiary characteristics, however, ACOs serving the most deprived had a savings rate that was 2.3 percentage points lower than those serving the least deprived.

CONCLUSIONS

ACOs serving deprived communities generate less savings. These findings are important to primary care practices, payers, and policy makers anticipating continued ACO expansion, if population health is to be achieved equitably.

Mental Workload Relating Health Information System - A Literature Review

BACKGROUND

Assessing Mental Workload related to Health Information Systems can help to analyze weak points of the use of Health Information Systems and in health care work processes. Our objectives were to give an overview of current research and applied measurement methods as well as gaining insights into influencing factors of mental workload on the use of health information systems and vice versa.

METHODS

We applied a structured literature research by searching for "mental workload" on PubMed. Studies were included into our review if they assessed related to Health Information Systems.

RESULTS

The research in PubMed led to 124 articles, resulting in 17 papers taken into in-depth analyses. We identified three categories referring to different study design types. Additionally, articles showed that mental workload was influenced by using health information systems and vice versa.

DISCUSSION

The review was limited to only one database but revealed that future research with sociotechnical focus including mental workload is necessary.

CONCLUSION

In contrast to the high relevance only a few articles address mental workload in Health Information systems. The quality of the studies in terms of evidence and external validity appears to be largely in need of development and should be improved in ongoing research.

A Privacy-Preserving Infrastructure for Analyzing Personal Health Data in a Vertically Partitioned Scenario

It is widely anticipated that the use and analysis of health-related big data will enable further understanding and improvements in human health and wellbeing. Here, we propose an innovative infrastructure, which supports secure and privacy-preserving analysis of personal health data from multiple providers with different governance policies. Our objective is to use this infrastructure to explore the relation between Type 2 Diabetes Mellitus status and healthcare costs. Our approach involves the use of distributed machine learning to analyze vertically partitioned data from the Maastricht Study, a prospective population-based cohort study, and data from the official statistics agency of the Netherlands, Statistics Netherlands (Centraal Bureau voor de Statistiek; CBS). This project seeks an optimal solution accounting for scientific, technical, and ethical/legal challenges. We describe these challenges, our progress towards addressing them in a practical use case, and a simulation experiment.

A Framework for Applied AI in Healthcare

Significant efforts are being made to develop artificial intelligence technologies for health settings. In a health system that has been notoriously slow to adopt innovative technologies, it is important to consider the implementation of a new technology early in the development stage, especially one that will have added challenges of trust and transparency. To facilitate this process, an implementation framework for artificial intelligence technologies in clinical settings has been created.

Generalizability of Readability Models for Medical Terms

Detection of difficult for understanding words is a crucial task for ensuring the proper understanding of medical texts such as diagnoses and drug instructions. We propose to combine supervised machine learning algorithms using various features with word embeddings which contain context information of words. Data in French are manually cross-annotated by seven annotators. On the basis of these data, we propose cross-validation scenarios in order to test the generalization ability of models to detect the difficulty of medical words. On data provided by seven annotators, we show that the models are generalizable from one annotator to another.