Innovations in research and clinical care using patient-generated health data.

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

Power to the People: Why person-generated health data is important for pharmacoepidemiology.

Person-generated health data (PGHD) are valuable to study outcomes relevant to everyday living, to obtain information not otherwise available, for long-term follow-up and in situations where decisions cannot wait for traditional clinical research to be completed. While there is no dispute that these data are subject to bias, insights gained may be better than an information void, provided the biases are understood and acknowledged. People will share information known uniquely to them about exposures that may affect drug tolerance, safety and effectiveness, e.g., using non-prescription and complementary medications, alcohol, tobacco, illicit drugs, exercise, etc. Patients may be the best source of safety information when long-term follow-up is needed, e.g., the 5-15-year follow-up required for some gene therapies. Validation studies must be performed to evaluate what people can accurately report and when supplementary confirmation information is needed. But PGHD has already proven valuable in quantifying and contrasting COVID-19 vaccine benefits and risks, and for evaluating disease transmission and the accuracy of COVID-19 testing. Going forward, PGHD will be used for patient-measured and patient-relevant outcomes, including regulatory purposes, and will be linked to broader health data networks using tokenization, becoming a mainstay for signals about risks and benefits for diverse populations.

Validation of algorithms in studies based on routinely collected health data: general principles.

Clinicians, researchers, regulators, and other decision-makers increasingly rely on evidence from real-world data (RWD), including data routinely accumulating in health and administrative databases. RWD studies often rely on algorithms to operationalize variable definitions. An algorithm is a combination of codes or concepts used to identify persons with a specific health condition or characteristic. Establishing the validity of algorithms is a prerequisite for generating valid study findings that can ultimately inform evidence-based health care. This paper aims to systematize terminology, methods, and practical considerations relevant to the conduct of validation studies of RWD-based algorithms. We discuss measures of algorithm accuracy; gold/reference standard; study size; prioritizing accuracy measures; algorithm portability; and implication for interpretation. Information bias is common in epidemiologic studies, underscoring the importance of transparency in decisions regarding choice and prioritizing measures of algorithm validity. The validity of an algorithm should be judged in the context of a data source, and one size does not fit all. Prioritizing validity measures within a given data source depends on the role of a given variable in the analysis (eligibility criterion, exposure, outcome or covariate). Validation work should be part of routine maintenance of RWD sources.

Safety and effectiveness of mogamulizumab in relapsed or refractory CC chemokine receptor 4-positive peripheral T-cell lymphoma and relapsed or refractory cutaneous T-cell lymphoma: A post-marketing surveillance in Japan.

Mogamulizumab is a humanized antibody targeting CC chemokine receptor 4 (CCR4). This post-marketing surveillance was conducted in Japan as a regulatory requirement from 2014 to 2020 to ensure the safety and effectiveness of mogamulizumab in patients with relapsed or refractory (r/r) CCR4-positive peripheral T-cell lymphoma (PTCL) or r/r cutaneous T-cell lymphoma (CTCL). Safety and effectiveness data were collected for up to 31 weeks after treatment initiation. A total of 142 patients were registered; safety was evaluated in 136 patients. The median number of doses was 8.0 (range, 1-18). The main reasons for treatment termination were insufficient response (22.1%) and adverse events (13.2%). The frequency of any grade adverse drug reaction was 57.4%, including skin disorders (26.5%), infections and immune system disorders (16.2%), and infusion-related reactions (13.2%). Graft-versus-host disease, grade 2, developed in one of two patients who underwent allogeneic-hematopoietic stem cell transplantation after receiving mogamulizumab. Effectiveness was evaluated in 131 patients (103 with PTCL; 28 with CTCL). The best overall response rate was 45.8% (PTCL, 47.6%; CTCL, 39.3%). At week 31, the survival rate was 69.0% (95% confidence interval, 59.8%-76.5%) [PTCL, 64.4% (54.0%-73.0%); CTCL, 90.5% (67.0%-97.5%)]. Safety and effectiveness were comparable between patients <70 and ≥ 70 years old and between those with relapsed and refractory disease. The safety and effectiveness of mogamulizumab for PTCL and CTCL in the real world were comparable with the data reported in previous clinical trials. Clinical Trial Registration.

Use of pragmatic randomized trials in multiple sclerosis: A systematic overview.

BACKGROUND: Pragmatic trials are increasingly recognized for providing real-world evidence on treatment choices. OBJECTIVE: The objective of this study is to investigate the use and characteristics of pragmatic trials in multiple sclerosis (MS). METHODS: Systematic literature search and analysis of pragmatic trials on any intervention published up to 2022. The assessment of pragmatism with PRECIS-2 (PRagmatic Explanatory Continuum Indicator Summary-2) is performed. RESULTS: We identified 48 pragmatic trials published 1967-2022 that included a median of 82 participants (interquartile range (IQR) = 42-160) to assess typically supportive care interventions (n = 41; 85%). Only seven trials assessed drugs (15%). Only three trials (6%) included >500 participants. Trials were mostly from the United Kingdom (n = 18; 38%), Italy (n = 6; 13%), the United States and Denmark (each n = 5; 10%). Primary outcomes were diverse, for example, quality-of-life, physical functioning, or disease activity. Only 1 trial (2%) used routinely collected data for outcome ascertainment. No trial was very pragmatic in all design aspects, but 14 trials (29%) were widely pragmatic (i.e. PRECIS-2 score ⩾ 4/5 in all domains). CONCLUSION: Only few and mostly small pragmatic trials exist in MS which rarely assess drugs. Despite the widely available routine data infrastructures, very few trials utilize them. There is an urgent need to leverage the potential of this pioneering study design to provide useful randomized real-world evidence.

Use of multiple sclerosis disease-modifying therapies during pregnancy in France: Nationwide study between 2010 and 2021.

BACKGROUND: Multiple sclerosis (MS) frequently affects women of childbearing age and pregnant women. OBJECTIVE: To assess the use of MS disease-modifying therapies (DMTs) during pregnancy in France over the last decade, marked by an increasing DMTs availability. METHODS: All pregnancies ended from April 2010 to December 2021 in women with MS were identified based on the nationwide Mother-Child Register EPI-MERES, built from the French National Health Data System (Système National des Données de Santé (SNDS)). RESULTS: Of a total of 20,567 pregnancies in women with MS, 7587 were exposed to DMT. The number of DMT-exposed pregnancies markedly increased from 1079 in 2010-2012 to 2413 in 2019-2021 (+124%), especially those exposed to glatiramer acetate, natalizumab, dimethyl fumarate, and anti-CD20. Among pregnancies of women on DMT 6 months before pregnancy, 78.0% underwent DMT discontinuation and 7.6% switched DMT, generally before (33.0% and 77.0%, respectively) or during the first trimester of pregnancy (58.3% and 17.8%, respectively). DMT discontinuation decreased from 84.0% in 2010-2012 to 72.4% in 2019-2021 and was less frequent among women aged ⩾35 years and those socioeconomically disadvantaged. CONCLUSION: Despite MS therapeutic management adaptations to pregnancy, exposure during pregnancy to treatments whose safety profile has not yet been clearly established has increased sharply over the last decade.

Ascertaining the Francophone population in Ontario: validating the language variable in health data.

BACKGROUND: Language barriers can impact health care and outcomes. Valid and reliable language data is central to studying health inequalities in linguistic minorities. In Canada, language variables are available in administrative health databases; however, the validity of these variables has not been studied. This study assessed concordance between language variables from administrative health databases and language variables from the Canadian Community Health Survey (CCHS) to identify Francophones in Ontario. METHODS: An Ontario combined sample of CCHS cycles from 2000 to 2012 (from participants who consented to link their data) was individually linked to three administrative databases (home care, long-term care [LTC], and mental health admissions). In total, 27,111 respondents had at least one encounter in one of the three databases. Language spoken at home (LOSH) and first official language spoken (FOLS) from CCHS were used as reference standards to assess their concordance with the language variables in administrative health databases, using the Cohen kappa, sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV). RESULTS: Language variables from home care and LTC databases had the highest agreement with LOSH (kappa = 0.76 [95%CI, 0.735-0.793] and 0.75 [95%CI, 0.70-0.80], respectively) and FOLS (kappa = 0.66 for both). Sensitivity was higher with LOSH as the reference standard (75.5% [95%CI, 71.6-79.0] and 74.2% [95%CI, 67.3-80.1] for home care and LTC, respectively). With FOLS as the reference standard, the language variables in both data sources had modest sensitivity (53.1% [95%CI, 49.8-56.4] and 54.1% [95%CI, 48.3-59.7] in home care and LTC, respectively) but very high specificity (99.8% [95%CI, 99.7-99.9] and 99.6% [95%CI, 99.4-99.8]) and predictive values. The language variable from mental health admissions had poor agreement with all language variables in the CCHS. CONCLUSIONS: Language variables in home care and LTC health databases were most consistent with the language often spoken at home. Studies using language variables from administrative data can use the sensitivity and specificity reported from this study to gauge the level of mis-ascertainment error and the resulting bias.

Assessing cardiovascular disease risk in women with a history of hypertensive disorders of pregnancy: A guidance paper for studies using administrative data.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a major cause of maternal morbidity and mortality, and their association with increased cardiovascular disease (CVD) risk represents a major public health concern. However, assessing CVD risk in women with a history of these conditions presents unique challenges, especially when studies are carried out using routinely collected data. OBJECTIVES: To summarise and describe key challenges related to the design and conduct of administrative studies assessing CVD risk in women with a history of HDP and provide concrete recommendations for addressing them in future research. METHODS: This is a methodological guidance paper. RESULTS: Several conceptual and methodological factors related to the data-generating mechanism and study conceptualisation, design/data management and analysis, as well as the interpretation and reporting of study findings should be considered and addressed when designing and carrying out administrative studies on this topic. Researchers should develop an a priori conceptual framework within which the research question is articulated, important study variables are identified and their interrelationships are carefully considered. CONCLUSIONS: To advance our understanding of CVD risk in women with a history of HDP, future studies should carefully consider and address the conceptual and methodological considerations outlined in this guidance paper. In highlighting these challenges, and providing specific recommendations for how to address them, our goal is to improve the quality of research carried out on this topic.

Perspectives of healthcare professionals and people living with HIV in dialogue: on information sharing to improve communication at the consultation.

We report on a qualitative Group Survey study involving four healthcare professionals (HCPs) and eight people living with HIV who were recipients of care in the United Kingdom (UK). The survey aimed to bring participants' perspectives into dialogue and establish consensus about how communication between HCPs delivering HIV care and their patients could be improved in the context of the routine care consultation. Responses from both parties were anonymously collated, thematically analysed, and shared back with participants in two subsequent survey rounds to support consensus-building on matters of concern and identify thematic insights. In this paper, we report three themes for informing future designs of tools and services to support communication between patients and HCPs: Patient-clinician relationship for trusted sharing; Self-reporting psychosocial information to support Whole-person care; and Perceived barriers for online trusted sharing with HCPs. Our findings highlight key areas of concern and further investigation is needed to understand how self-reported information may be meaningfully captured, interpreted and processed by HCPs in ways that are trusted by patients who voice privacy and security concerns.

Desiderata for discoverability and FAIR adoption of health data hubs.

BACKGROUND: The future European Health Research and Innovation Cloud (HRIC), as fundamental part of the European Health Data Space (EHDS), will promote the secondary use of data and the capabilities to push the boundaries of health research within an ethical and legally compliant framework that reinforces the trust of patients and citizens. OBJECTIVE: This study aimed to analyse health data management mechanisms in Europe to determine their alignment with FAIR principles and data discovery generating best. practices for new data hubs joining the HRIC ecosystem. In this line, the compliance of health data hubs with FAIR principles and data discovery were assessed, and a set of best practices for health data hubs was concluded. METHODS: A survey was conducted in January 2022, involving 99 representative health data hubs from multiple countries, and 42 responses were obtained in June 2022. Stratification methods were employed to cover different levels of granularity. The survey data was analysed to assess compliance with FAIR and data discovery principles. The study started with a general analysis of survey responses, followed by the creation of specific profiles based on three categories: organization type, function, and level of data aggregation. RESULTS: The study produced specific best practices for data hubs regarding the adoption of FAIR principles and data discoverability. It also provided an overview of the survey study and specific profiles derived from category analysis, considering different types of data hubs. CONCLUSIONS: The study concluded that a significant number of health data hubs in Europe did not fully comply with FAIR and data discovery principles. However, the study identified specific best practices that can guide new data hubs in adhering to these principles. The study highlighted the importance of aligning health data management mechanisms with FAIR principles to enhance interoperability and reusability in the future HRIC.

Converting OMOP CDM to phenopackets: A model alignment and patient data representation evaluation.

OBJECTIVE: This study aims to promote interoperability in precision medicine and translational research by aligning the Observational Medical Outcomes Partnership (OMOP) and Phenopackets data models. Phenopackets is an expert knowledge-driven schema designed to facilitate the storage and exchange of multimodal patient data, and support downstream analysis. The first goal of this paper is to explore model alignment by characterizing the common data models using a newly developed data transformation process and evaluation method. Second, using OMOP normalized clinical data, we evaluate the mapping of real-world patient data to Phenopackets. We evaluate the suitability of Phenopackets as a patient data representation for real-world clinical cases. METHODS: We identified mappings between OMOP and Phenopackets and applied them to a real patient dataset to assess the transformation's success. We analyzed gaps between the models and identified key considerations for transforming data between them. Further, to improve ambiguous alignment, we incorporated Unified Medical Language System (UMLS) semantic type-based filtering to direct individual concepts to their most appropriate domain and conducted a domain-expert evaluation of the mapping's clinical utility. RESULTS: The OMOP to Phenopacket transformation pipeline was executed for 1,000 Alzheimer's disease patients and successfully mapped all required entities. However, due to missing values in OMOP for required Phenopacket attributes, 10.2 % of records were lost. The use of UMLS-semantic type filtering for ambiguous alignment of individual concepts resulted in 96 % agreement with clinical thinking, increased from 68 % when mapping exclusively by domain correspondence. CONCLUSION: This study presents a pipeline to transform data from OMOP to Phenopackets. We identified considerations for the transformation to ensure data quality, handling restrictions for successful Phenopacket validation and discrepant data formats. We identified unmappable Phenopacket attributes that focus on specialty use cases, such as genomics or oncology, which OMOP does not currently support. We introduce UMLS semantic type filtering to resolve ambiguous alignment to Phenopacket entities to be most appropriate for real-world interpretation. We provide a systematic approach to align OMOP and Phenopackets schemas. Our work facilitates future use of Phenopackets in clinical applications by addressing key barriers to interoperability when deriving a Phenopacket from real-world patient data.

Toward the European Health Data Space: The IMPaCT-Data secure infrastructure for EHR-based precision medicine research.

BACKGROUND: Art. 50 of the proposal for a Regulation on the European Health Data Space (EHDS) states that "health data access bodies shall provide access to electronic health data only through a secure processing environment, with technical and organizational measures and security and interoperability requirements". OBJECTIVE: To identify specific security measures that nodes participating in health data spaces shall implement based on the results of the IMPaCT-Data project, whose goal is to facilitate the exchange of electronic health records (EHR) between public entities based in Spain and the secondary use of this information for precision medicine research in compliance with the General Data Protection Regulation (GDPR). DATA AND METHODS: This article presents an analysis of 24 out of a list of 72 security measures identified in the Spanish National Security Scheme (ENS) and adopted by members of the federated data infrastructure developed during the IMPaCT-Data project. RESULTS: The IMPaCT-Data case helps clarify roles and responsibilities of entities willing to participate in the EHDS by reconciling technical system notions with the legal terminology. Most relevant security measures for Data Space Gatekeepers, Enablers and Prosumers are identified and explained. CONCLUSION: The EHDS can only be viable as long as the fiduciary duty of care of public health authorities is preserved; this implies that the secondary use of personal data shall contribute to the public interest and/or to protect the vital interests of the data subjects. This condition can only be met if all nodes participating in a health data space adopt the appropriate organizational and technical security measures necessary to fulfill their role.

Validation of an algorithm for identifying incident cancer cases based on long-term illness and diagnosis related group program data from the French National Health Insurance Information System (SNDS).

PURPOSE: Three generic claims-based algorithms based on the Illness Classification of Diseases (10th revision- ICD-10) codes, French Long-Term Illness (LTI) data, and the Diagnosis Related Group program (DRG) were developed to identify retirees with cancer using data from the French national health insurance information system (Système national des données de santé or SNDS) which covers the entire French population. The present study aimed to calculate the algorithms' performances and to describe false positives and negatives in detail. METHODS: Between 2011 and 2016, data from 7544 participants of the French retired self-employed craftsperson cohort (ESPrI) were first matched to the SNDS data, and then toFrench population-based cancer registries data, used as the gold standard. Performance indicators, such as sensitivity and positive predictive values, were estimated for the three algorithms in a subcohort of ESPrI. RESULTS: The third algorithm, which combined the LTI and DRG program data, presented the best sensitivities (90.9%-100%) and positive predictive values (58.1%-95.2%) according to cancer sites. The majority of false positives were in fact nearby organ sites (e.g., stomach for esophagus) and carcinoma in situ. Most false negatives were probably due to under declaration of LTI. CONCLUSION: Validated algorithms using data from the SNDS can be used for passive epidemiological follow-up for some cancer sites in the ESPrI cohort.

Validation of new medication use algorithms as proxies for worsening disease activity in patients with juvenile idiopathic arthritis.

PURPOSE: To facilitate claims-based research on populations with juvenile idiopathic arthritis (JIA), we sought to validate an algorithm of new medication use as a proxy for worsening JIA disease activity. METHODS: Using electronic health record data from three pediatric centers, we defined new JIA medication use as (re)initiation of disease-modifying antirheumatic drugs or glucocorticoids (oral or intra-articular). Data were collected from 201 randomly selected subjects with (101) or without (100) new medication use. We assessed the positive predictive value (PPV) and negative predictive value (NPV) based on a reference standard of documented worsening of JIA disease activity. The algorithm was refined to optimize test characteristics. RESULTS: Overall, the medication-based algorithm had suboptimal performance in representing worsening JIA disease activity (PPV 69.3%, NPV 77.1%). However, algorithm performance improved for definitions specifying longer times after JIA diagnosis (≥1-year post-diagnosis: PPV 82.9%, NPV 80.0%) or after initiation of prior JIA treatment (≥1-year post-treatment: PPV 89.7%, NPV 80.0%). CONCLUSION: An algorithm for new JIA medication use appears to be a reasonable proxy for worsening JIA disease activity, particularly when specifying new use ≥1 year since initiating a prior JIA medication. This algorithm will be valuable for conducting research on JIA populations within administrative claims databases.

Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project.

BACKGROUND: Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored. METHODS: PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS' practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software. DISCUSSION: Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.

Health information management systems and practices in conflict-affected settings: the case of northwest Syria.

BACKGROUND: In conflict settings, as it is the case in Syria, it is crucial to enhance health information management to facilitate an effective and sustainable approach to strengthening health systems in such contexts. In this study, we aim to provide a baseline understanding of the present state of health information management in Northwest Syria (NWS) to better plan for strengthening the health information system of the area that is transitioning to an early-recovery stage. METHODS: A combination of questionnaires and subsequent interviews was used for data collection. Purposive sampling was used to select twenty-one respondents directly involved in managing and directing different domains of health information in the NWS who worked with local NGOs, INGOs, UN-agencies, or part of the Health Working Group. A scoring system for each public health domain was constructed based on the number and quality of the available datasets for these domains, which were established by Checci and others. RESULTS & CONCLUSIONS: Reliable and aggregate health information in the NWS is limited, despite some improvements made over the past decade. The conflict restricted and challenged efforts to establish a concentrated and harmonized HIS in the NWS, which led to a lack of leadership, poor coordination, and duplication of key activities. Although the UN established the EWARN and HeRAMS as common data collection systems in the NWS, they are directed toward advocacy and managed by external experts with little participation or access from local stakeholders to these datasets. RECOMMENDATIONS: There is a need for participatory approaches and the empowerment of local actors and local NGOs, cooperation between local and international stakeholders to increase access to data, and a central domain for planning, organization, and harmonizing the process. To enhance the humanitarian health response in Syria and other crisis areas, it is imperative to invest in data collection and utilisation, mHealth and eHealth technologies, capacity building, and robust technical and autonomous leadership.

Digital medication and patients' right of autonomy in Spain.

The progress the Internet has experienced in recent years has brought about huge changes and social transformation in all aspects of our lives. One such aspect greatly impacted has been our health, where we can talk about the existence of an 'Internet of Medical Things'. Amid this digital drift, we have seen the development of pharmaceutical drugs that provide information to patients and their attending healthcare teams concerning medication, doses ingested, and time of ingestion. These are digital pills or digital medication. In this context, the purpose of my paper is to analyze the ethical and legal impact of digital medication, further analyzing the implications concerning the right of service users to make decisions over their own health in Spain.

Utilization of routine health data and its determinants among healthcare workers in public health facilities of harari region, eastern Ethiopia.

BACKGROUND: Routine health information is the pillar of the planning and management of health services and plays a vital role in effective and efficient health service delivery, decision making, and program improvement. Little is known about evidence-based actions to successively advance the use of information for decision making. Therefore, this study aimed to assess the level and determinants of routine health data utilization among health workers in public health facilities in the Harari region, Ethiopia. METHODS: An institutional-based cross-sectional study design was used from June 1 to July 31, 2020. A total of 410 health care providers from two hospitals and five health centers were selected using a simple random sampling technique. Data were collected through a structured questionnaire complemented by an observational checklist. The collected data were thoroughly checked, coding, and entered into Epi-data version 4.6 before being transferred to Stata version 14 for analysis. Frequency and cross-tabulations were performed. To measure factors associated with routine use of health data, bivariate and multivariate logistic regression analyzes were performed. The odds ratio with a 95% CI was calculated, and then a p-value of less than 0.05 was considered significant. RESULT: The general utilization of routine health data was 65.6%. The use of routine health data was significantly associated with healthcare workers who had a positive attitude towards data [AOR = 4 (2.3-6.9)], received training [AOR = 2.1 (1.3-3.6)], had supportive supervision [AOR = 3.6 (2.1-6.2)], received regular feedback [AOR = 2.9 (1.7-5.0)] and perceived a culture of information use [AOR = 2.5 (1.3-4.6)]. CONCLUSIONS: Sixty percent of health professionals had used routine health data utilization. Training, supervision, feedback, and the perceived culture of information were independently associated with the use of routine health data utilization. Therefore, it is critical to focus on improving data utilization practices by addressing factors that influence the use of routine health data.

Evaluating the coding accuracy of type 2 diabetes mellitus among patients with non-alcoholic fatty liver disease.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) describes a spectrum of chronic fattening of liver that can lead to fibrosis and cirrhosis. Diabetes has been identified as a major comorbidity that contributes to NAFLD progression. Health systems around the world make use of administrative data to conduct population-based prevalence studies. To that end, we sought to assess the accuracy of diabetes International Classification of Diseases (ICD) coding in administrative databases among a cohort of confirmed NAFLD patients in Calgary, Alberta, Canada. METHODS: The Calgary NAFLD Pathway Database was linked to the following databases: Physician Claims, Discharge Abstract Database, National Ambulatory Care Reporting System, Pharmaceutical Information Network database, Laboratory, and Electronic Medical Records. Hemoglobin A1c and diabetes medication details were used to classify diabetes groups into absent, prediabetes, meeting glycemic targets, and not meeting glycemic targets. The performance of ICD codes among these groups was compared to this standard. Within each group, the total numbers of true positives, false positives, false negatives, and true negatives were calculated. Descriptive statistics and bivariate analysis were conducted on identified covariates, including demographics and types of interacted physicians. RESULTS: A total of 12,012 NAFLD patients were registered through the Calgary NAFLD Pathway Database and 100% were successfully linked to the administrative databases. Overall, diabetes coding showed a sensitivity of 0.81 and a positive predictive value of 0.87. False negative rates in the absent and not meeting glycemic control groups were 4.5% and 6.4%, respectively, whereas the meeting glycemic control group had a 42.2% coding error. Visits to primary and outpatient services were associated with most encounters. CONCLUSION: Diabetes ICD coding in administrative databases can accurately detect true diabetic cases. However, patients with diabetes who meets glycemic control targets are less likely to be coded in administrative databases. A detailed understanding of the clinical context will require additional data linkage from primary care settings.

A Taxonomy for Health Information Systems.

The health sector is highly digitized, which is enabling the collection of vast quantities of electronic data about health and well-being. These data are collected by a diverse array of information and communication technologies, including systems used by health care organizations, consumer and community sources such as information collected on the web, and passively collected data from technologies such as wearables and devices. Understanding the breadth of IT that collect these data and how it can be actioned is a challenge for the significant portion of the digital health workforce that interact with health data as part of their duties but are not for informatics experts. This viewpoint aims to present a taxonomy categorizing common information and communication technologies that collect electronic data. An initial classification of key information systems collecting electronic health data was undertaken via a rapid review of the literature. Subsequently, a purposeful search of the scholarly and gray literature was undertaken to extract key information about the systems within each category to generate definitions of the systems and describe the strengths and limitations of these systems.