Barriers to the Digitization of Health Information: A Qualitative and Quantitative Study in Kenya and Lao PDR Using a Cloud-Based Maternal and Child Registration System

Development of a patient satisfaction questionnaire (PSQ) for diabetes management in Thailand and Lao PDR

In a cross-sectional analytical study, a Patient Satisfaction Questionnaire (PSQ) for diabetes management was developed and tested in Thailand and Lao PDR. A systematic review of qualitative studies was conducted to formulate themes of the PSQ. The 20-item PSQ was prepared in Thai and translated to Lao, with subsequent backward translation. Both versions were tested for reliability and construct validity using confirmatory factor analysis and structural equation modeling. The study was performed at a university hospital in Thailand and two central hospitals in Vientiane, Lao PDR. There were 300 diabetic patients from Thailand (n = 150) and Lao PDR (n = 150). The 5-factor Thai version showed 74.52% of total explained variance with good internal consistency and satisfactory goodness-of-fit indices (χ2/df = 1.91, GFI = 0.83, CFI = 0.98, SRMR = 0.063, RMSEA = 0.078). The five factors were 1) Standard of Service, 2) Diabetic Service, 3) Competency of Providers, 4) Competency of Pharmacists, and 5) Communication with Providers. For the Lao version, 20 items showed a 3-factor structure with a total explained variance of 71.09%. Goodness-of-fit indices for the Lao model were satisfactory (χ2/df = 2.45, GFI = 0.78, CFI = 0.95, SRMR = 0.075 and RMSEA = 0.095). The results showed the PSQ Thai and Lao versions were valid and reliable for assessing patient satisfaction with diabetes management, however more testing of the questionnaire is appropriate.

Development of novel composite data quality scores to evaluate facility-level data quality in electronic data in Kenya: a nationwide retrospective cohort study

BACKGROUND

In this evaluation, we aim to strengthen Routine Health Information Systems (RHIS) through the digitization of data quality assessment (DQA) processes. We leverage electronic data from the Kenya Health Information System (KHIS) which is based on the District Health Information System version 2 (DHIS2) to perform DQAs at scale. We provide a systematic guide to developing composite data quality scores and use these scores to assess data quality in Kenya.

METHODS

We evaluated 187 HIV care facilities with electronic medical records across Kenya. Using quarterly, longitudinal KHIS data from January 2011 to June 2018 (total N = 30 quarters), we extracted indicators encompassing general HIV services including services to prevent mother-to-child transmission (PMTCT). We assessed the accuracy (the extent to which data were correct and free of error) of these data using three data-driven composite scores: 1) completeness score; 2) consistency score; and 3) discrepancy score. Completeness refers to the presence of the appropriate amount of data. Consistency refers to uniformity of data across multiple indicators. Discrepancy (measured on a Z-scale) refers to the degree of alignment (or lack thereof) of data with rules that defined the possible valid values for the data.

RESULTS

A total of 5,610 unique facility-quarters were extracted from KHIS. The mean completeness score was 61.1% [standard deviation (SD) = 27%]. The mean consistency score was 80% (SD = 16.4%). The mean discrepancy score was 0.07 (SD = 0.22). A strong and positive correlation was identified between the consistency score and discrepancy score (correlation coefficient = 0.77), whereas the correlation of either score with the completeness score was low with a correlation coefficient of -0.12 (with consistency score) and -0.36 (with discrepancy score). General HIV indicators were more complete, but less consistent, and less plausible than PMTCT indicators.

CONCLUSION

We observed a lack of correlation between the completeness score and the other two scores. As such, for a holistic DQA, completeness assessment should be paired with the measurement of either consistency or discrepancy to reflect distinct dimensions of data quality. Given the complexity of the discrepancy score, we recommend the simpler consistency score, since they were highly correlated. Routine use of composite scores on KHIS data could enhance efficiencies in DQA at scale as digitization of health information expands and could be applied to other health sectors beyondHIV clinics.

Global, regional, and national trends in mesothelioma burden from 1990 to 2019 and the predictions for the next two decades

Objectives

We aimed to analyze the secular trends in mesothelioma burden, the effect of age, period, and birth cohort, and project the global burden over time.

Material and methods

Based on the mesothelioma incidence, mortality, and Disability-Adjusted Life Years (DALYs) data from 1990 to 2019 in Global Burden of Diseases (GBD) database, the annual percentage change (APC) and average annual percent change (AAPC), calculated from joinpoint regression model, was used to describe the burden trends. An age-period-cohort model was utilized to disentangle age, period, and birth cohort effects on mesothelioma incidence and mortality trends. The mesothelioma burden was projected by the Bayesian age-period-cohort (BAPC) model.

Results

Globally, there were the significant declines in age-standardized incidence rate (ASIR) (AAPC = -0.4, 95%CI: -0.6,-0.3, P < 0.001), age-standardized mortality rate (ASMR) (AAPC = -0.3, 95%CI: -0.4,-0.2, P < 0.001), and age-standardized DALY rate (ASDR) (AAPC = -0.5, 95%CI: -0.6,-0.4, P < 0.001) of mesothelioma overall 30 years. For regions, Central Europe presented the most distinct increases and the most substantial decrease was observed in Andean Latin America on all ASRs (age-standardized rates) from 1990 to 2019. At national level, the largest annualized growth for full-range trends of incidence, mortality, and DALYs was in Georgia. Conversely, the fastest descent of all ASRs was observed in Peru. The ASIR, ASMR, and ASDR in 2039 predicted 0.33, 0.27, and 6.90 per 100,000, respectively.

Conclusions

The global burden of mesothelioma declined over the past 30 years, with variability across regions and countries/territories, and this trend will continue in the future.

Digital Transformation in Healthcare: Technology Acceptance and Its Applications

Technological innovation has become an integral aspect of our daily life, such as wearable and information technology, virtual reality and the Internet of Things which have contributed to transforming healthcare business and operations. Patients will now have a broader range and more mindful healthcare choices and experience a new era of healthcare with a patient-centric culture. Digital transformation determines personal and institutional health care. This paper aims to analyse the changes taking place in the field of healthcare due to digital transformation. For this purpose, a systematic bibliographic review is performed, utilising Scopus, Science Direct and PubMed databases from 2008 to 2021. Our methodology is based on the approach by Wester and Watson, which classify the related articles based on a concept-centric method and an ad hoc classification system which identify the categories used to describe areas of literature. The search was made during August 2022 and identified 5847 papers, of which 321 fulfilled the inclusion criteria for further process. Finally, by removing and adding additional studies, we ended with 287 articles grouped into five themes: information technology in health, the educational impact of e-health, the acceptance of e-health, telemedicine and security issues.

Data analytics in a clinical setting: Applications to understanding breathing patterns and their relevance to neonatal disease

In this review, we focus on the use of contemporary linear and non-linear data analytics as well as machine learning/artificial intelligence algorithms to inform treatment of pediatric patients. We specifically focus on methods used to quantify changes in breathing that can lead to increased risk for apnea of prematurity, retinopathy of prematurity (ROP), necrotizing enterocolitis (NEC) and provide a list of potentially useful algorithms that comprise a suite of software tools to enhance prediction of outcome. Next, we provide a brief overview of machine learning/artificial intelligence methods and applications within the sphere of perinatal care. Finally, we provide an overview of the infrastructure needed to use these tools in a clinical setting for real-time data acquisition, data synchrony, data storage and access, and bedside data visualization to assist in clinical decision making and support the medical informatics mission. Our goal is to provide an overview and inspire other investigators to adopt these tools for their own research and optimization of perinatal patient care.

Implementation Outcomes Assessment of a Digital Clinical Support Tool for Intrapartum Care in Rural Kenya: Observational Analysis

Background

iDeliver, a digital clinical support system for maternal and neonatal care, was developed to support quality of care improvements in Kenya.

Objective

Taking an implementation research approach, we evaluated the adoption and fidelity of iDeliver over time and assessed the feasibility of its use to provide routine Ministry of Health (MOH) reports.

Methods

We analyzed routinely collected data from iDeliver, which was implemented at the Transmara West Sub-County Hospital from December 2018 to September 2020. To evaluate its adoption, we assessed the proportion of actual facility deliveries that was recorded in iDeliver over time. We evaluated the fidelity of iDeliver use by studying the completeness of data entry by care providers during each stage of the labor and delivery workflow and whether the use reflected iDeliver’s envisioned function. We also examined the data completeness of the maternal and neonatal indicators prioritized by the Kenya MOH.

Results

A total of 1164 deliveries were registered in iDeliver, capturing 45.31% (1164/2569) of the facility’s deliveries over 22 months. This uptake of registration improved significantly over time by 6.7% (SE 2.1) on average in each quarter-year (P=.005), from 9.6% (15/157) in the fourth quarter of 2018 to 64% (235/367) in the third quarter of 2020. Across iDeliver’s workflow, the overall completion rate of all variables improved significantly by 2.9% (SE 0.4) on average in each quarter-year (P<.001), from 22.25% (257/1155) in the fourth quarter of 2018 to 49.21% (8905/18,095) in the third quarter of 2020. Data completion was highest for the discharge-labor summary stage (16,796/23,280, 72.15%) and lowest for the labor signs stage (848/5820, 14.57%). The completion rate of the key MOH indicators also improved significantly by 4.6% (SE 0.5) on average in each quarter-year (P<.001), from 27.1% (69/255) in the fourth quarter of 2018 to 83.75% (3346/3995) in the third quarter of 2020.

Conclusions

iDeliver’s adoption and data completeness improved significantly over time. The assessment of iDeliver’ use fidelity suggested that some features were more easily used because providers had time to enter data; however, there was low use during active childbirth, which is when providers are necessarily engaged with the woman and newborn. These insights on the adoption and fidelity of iDeliver use prompted the team to adapt the application to reflect the users’ culture of use and further improve the implementation of iDeliver.