Routine health information system in the health facilities in Yaoundé-Cameroon: assessing the gaps for strengthening.

BACKGROUND: Management of health data and its use for informed-decision making is a challenging health sector aspect in developing countries. Monitoring and evaluation of health interventions for meeting health-related Sustainable Development Goals (SDGs), and Cameroon Health Sector Strategy (HSS) targets is facilitated through evidence-based decision-making and public health action. Thus, a routine health information system (RHIS) producing quality data is imperative. The objective of this study was to assess the RHIS in the health facilities (HFs) in Yaoundé in order to identify gaps and weaknesses and to propose measures for strengthening. METHODS: A health facility-based cross-sectional descriptive study was carried out in the six health districts (HDs) of Yaoundé; followed by a qualitative aspect consisting of in-depth interviews of key informants at the Regional Health Office. HFs were selected using a stratified sampling method with probability proportional to the size of each HD. Data were collected (one respondent per HF) using the World Health Organization and MEASURE Evaluation RHIS rapid assessment tool. Data were entered into Microsoft Excel 2013 and analyzed with IBM-SPSS version 20. RESULTS: A total of 111 HFs were selected for the study. Respondents aged 24-60 years with an average of 38.3 ± 9.3 years; 58 (52.3%) male and 53(47.7%) female. Heads of HFs and persons in charge of statistics/data management were most represented with 45.0% and 21.6% respectively. All the twelve subdomains of the RHIS were adequately functioning at between 7 and 30%. These included Human Resources (7%), Data Analysis (10%), Information and Communication Technology (11%), Standards and System Design (15%), Policies and Planning (15%), Information Dissemination (16%), Data Demand and Use (16%), Management (18%), Data Needs (18%), Data Quality Assurance (20%), Collection and Management of Individual Client Data (26%), Collection, Management, and Reporting of Aggregated Facility Data (30%). CONCLUSIONS: The level of functioning of subdomains of the RHIS in Yaoundé was low; thus, immediate and district-specific strengthening actions should be implemented if health-related SDGs and HSS targets are to be met. A nation-wide assessment should be carried out in order to understand the determinants of these poor performances and to strengthen the RHIS.

LSDBs and How They Have Evolved.

Locus specific databases (LSDBs) make a key contribution to our understanding of heritable and acquired human disorders, disease susceptibility, and adverse drug reactions. As data have accumulated in LSDBs, a greater reliance on their use has arisen in clinical practice. Even though LSDBs have existed in recognizable form for only a quarter of a century, their origin lies in the manual cataloging of data that began around 50 years ago. Analysis and recording of sequence variation in the globin genes, and the proteins which they encode, can confidently be said to be the foundation for what we now refer to as LSDBs. Their growth over the years has primarily been underpinned by software developments and the advent of the World Wide Web. However, it is also important to recognize the evolution of reporting standards and reference sequences, without which accurate and consistent reporting of sequence variants would be impossible. Nowadays, LSDBs exist for many human protein-coding genes and the focus of efforts has moved toward minor tidying up of the variant reporting nomenclature and processes for assuring the completeness, correctness, and consistency of the data. The next 25 years will doubtless witness further developments in the evolution of LSDBs.

Quality Assurance of Health Wearables Data: Participatory Workshop on Barriers, Solutions, and Expectations.

BACKGROUND: The ubiquity of health wearables and the consequent production of patient-generated health data (PGHD) are rapidly escalating. However, the utilization of PGHD in routine clinical practices is still low because of data quality issues. There is no agreed approach to PGHD quality assurance; therefore, realizing the promise of PGHD requires in-depth discussion among diverse stakeholders to identify the data quality assurance challenges they face and understand their needs for PGHD quality assurance. OBJECTIVE: This paper reports findings from a workshop aimed to explore stakeholders' data quality challenges, identify their needs and expectations, and offer practical solutions. METHODS: A qualitative multi-stakeholder workshop was conducted as a half-day event on the campus of an Australian University located in a major health care precinct, namely the Melbourne Parkville Precinct. The 18 participants had experience of PGHD use in clinical care, including people who identified as health care consumers, clinical care providers, wearables suppliers, and health information specialists. Data collection was done by facilitators capturing written notes of the proceedings as attendees engaged in participatory design activities in written and oral formats, using a range of whole-group and small-group interactive methods. The collected data were analyzed thematically, using deductive and inductive coding. RESULTS: The participants' discussions revealed a range of technical, behavioral, operational, and organizational challenges surrounding PGHD, from the time when data are collected by patients to the time data are used by health care providers for clinical decision making. PGHD stakeholders found consensus on training and engagement needs, continuous collaboration among stakeholders, and development of technical and policy standards to assure PGHD quality. CONCLUSIONS: Assuring PGHD quality is a complex process that requires the contribution of all PGHD stakeholders. The variety and depth of inputs in our workshop highlighted the importance of co-designing guidance for PGHD quality guidance.

Patient-generated health data management and quality challenges in remote patient monitoring.

BACKGROUND: Patient-Generated Health Data (PGHD) in remote monitoring programs is a promising source of precise, personalized data, encouraged by expanding growth in the health technologies market. However, PGHD utilization in clinical settings is low. One of the critical challenges that impedes confident clinical use of PGHD is that these data are not managed according to any recognized approach for data quality assurance. OBJECTIVE: This article aims to identify the PGHD management and quality challenges that such an approach must address, as these are expressed by key PGHD stakeholder groups. MATERIALS AND METHODS: In-depth interviews were conducted with 20 experts who have experience in the use of PGHD in remote patient monitoring, including: healthcare providers, health information professionals within clinical settings, and commercial providers of remote monitoring solutions. Participants were asked to describe PGHD management processes in the remote monitoring programs in which they are involved, and to express their perspectives on PGHD quality challenges during the data management stages. RESULTS: The remote monitoring programs in the study did not follow clear PGHD management or quality assurance approach. Participants were not fully aware of all the considerations of PGHD quality. Digital health literacy, wearable accuracy, difficulty in data interpretation, and lack of PGHD integration with electronic medical record systems were among the key challenges identified that impact PGHD quality. CONCLUSION: Co-development of PGHD quality guidelines with relevant stakeholders, including patients, is needed to ensure that quality remote monitoring data from wearables is available for use in more precise and personalized patient care.

mosaicQA - A General Approach to Facilitate Basic Data Quality Assurance for Epidemiological Research.

BACKGROUND: Epidemiological studies are based on a considerable amount of personal, medical and socio-economic data. To answer research questions with reliable results, epidemiological research projects face the challenge of providing high quality data. Consequently, gathered data has to be reviewed continuously during the data collection period. OBJECTIVES: This article describes the development of the mosaicQA-library for non-statistical experts consisting of a set of reusable R functions to provide support for a basic data quality assurance for a wide range of application scenarios in epidemiological research. METHODS: To generate valid quality reports for various scenarios and data sets, a general and flexible development approach was needed. As a first step, a set of quality-related questions, targeting quality aspects on a more general level, was identified. The next step included the design of specific R-scripts to produce proper reports for metric and categorical data. For more flexibility, the third development step focussed on the generalization of the developed R-scripts, e.g. extracting characteristics and parameters. As a last step the generic characteristics of the developed R functionalities and generated reports have been evaluated using different metric and categorical datasets. RESULTS: The developed mosaicQA-library generates basic data quality reports for multivariate input data. If needed, more detailed results for single-variable data, including definition of units, variables, descriptions, code lists and categories of qualified missings, can easily be produced. CONCLUSIONS: The mosaicQA-library enables researchers to generate reports for various kinds of metric and categorical data without the need for computational or scripting knowledge. At the moment, the library focusses on the data structure quality and supports the assessment of several quality indicators, including frequency, distribution and plausibility of research variables as well as the occurrence of missing and extreme values. To simplify the installation process, mosaicQA has been released as an official R-package.

Implementation and results of an integrated data quality assurance protocol in a randomized controlled trial in Uttar Pradesh, India.

BACKGROUND: There are few published standards or methodological guidelines for integrating Data Quality Assurance (DQA) protocols into large-scale health systems research trials, especially in resource-limited settings. The BetterBirth Trial is a matched-pair, cluster-randomized controlled trial (RCT) of the BetterBirth Program, which seeks to improve quality of facility-based deliveries and reduce 7-day maternal and neonatal mortality and maternal morbidity in Uttar Pradesh, India. In the trial, over 6300 deliveries were observed and over 153,000 mother-baby pairs across 120 study sites were followed to assess health outcomes. We designed and implemented a robust and integrated DQA system to sustain high-quality data throughout the trial. METHODS: We designed the Data Quality Monitoring and Improvement System (DQMIS) to reinforce six dimensions of data quality: accuracy, reliability, timeliness, completeness, precision, and integrity. The DQMIS was comprised of five functional components: 1) a monitoring and evaluation team to support the system; 2) a DQA protocol, including data collection audits and targets, rapid data feedback, and supportive supervision; 3) training; 4) standard operating procedures for data collection; and 5) an electronic data collection and reporting system. Routine audits by supervisors included double data entry, simultaneous delivery observations, and review of recorded calls to patients. Data feedback reports identified errors automatically, facilitating supportive supervision through a continuous quality improvement model. RESULTS: The five functional components of the DQMIS successfully reinforced data reliability, timeliness, completeness, precision, and integrity. The DQMIS also resulted in 98.33% accuracy across all data collection activities in the trial. All data collection activities demonstrated improvement in accuracy throughout implementation. Data collectors demonstrated a statistically significant (p = 0.0004) increase in accuracy throughout consecutive audits. The DQMIS was successful, despite an increase from 20 to 130 data collectors. CONCLUSIONS: In the absence of widely disseminated data quality methods and standards for large RCT interventions in limited-resource settings, we developed an integrated DQA system, combining auditing, rapid data feedback, and supportive supervision, which ensured high-quality data and could serve as a model for future health systems research trials. Future efforts should focus on standardization of DQA processes for health systems research. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT02148952 . Registered on 13 February 2014.

Methodological issues for designing and conducting a multicenter, international clinical trial in Acute Stroke: Experience from ARTSS-2 trial.

BACKGROUND: We describe innovations in the study design and the efficient data coordination of a randomized multicenter trial of Argatroban in Combination with Recombinant Tissue Plasminogen Activator for Acute Stroke (ARTSS-2). METHODS: ARTSS-2 is a 3-arm, multisite/multiregional randomized controlled trials (RCTs) of two doses of Argatroban injection (low, high) in combination with recombinant tissue plasminogen activator (rt-PA) in acute ischemic stroke patients and rt-PA alone. We developed a covariate adaptive randomization program that balanced the study arms with respect to study site as well as hemorrhage after thrombolysis (HAT) score and presence of distal internal carotid artery occlusion (DICAO). We used simulation studies to validate performance of the randomization program before making any adaptations during the trial. For the first 90 patients enrolled in ARTSS-2, we evaluated performance of our randomization program using chi-square tests of homogeneity or extended Fisher's exact test. We also designed a four-step partly Bayesian safety stopping rule for low and high dose Argatroban arms. RESULTS: Homogeneity of the study arms was confirmed with respect to distribution of study site (UK sites vs. US sites, P=0.98), HAT score (0-2 vs. 3-5, P=1.0), and DICAO (N/A vs. No vs. Yes, P=0.97). Our stopping thresholds for safety of low and high dose Argatroban were not crossed. Despite challenges, data quality was assured. CONCLUSIONS: We recommend adaptive designs for randomization and Bayesian safety stopping rules for multisite Phase I/II RCTs for maintaining additional flexibility. Efficient data coordination could lead to improved data quality.

Using the CER Hub to ensure data quality in a multi-institution smoking cessation study.

Comparative effectiveness research (CER) studies involving multiple institutions with diverse electronic health records (EHRs) depend on high quality data. To ensure uniformity of data derived from different EHR systems and implementations, the CER Hub informatics platform developed a quality assurance (QA) process using tools and data formats available through the CER Hub. The QA process, implemented here in a study of smoking cessation services in primary care, used the 'emrAdapter' tool programmed with a set of quality checks to query large samples of primary care encounter records extracted in accord with the CER Hub common data framework. The tool, deployed to each study site, generated error reports indicating data problems to be fixed locally and aggregate data sharable with the central site for quality review. Across the CER Hub network of six health systems, data completeness and correctness issues were prevalent in the first iteration and were considerably improved after three iterations of the QA process. A common issue encountered was incomplete mapping of local EHR data values to those defined by the common data framework. A highly automated and distributed QA process helped to ensure the correctness and completeness of patient care data extracted from EHRs for a multi-institution CER study in smoking cessation.