{"doc_desc":{"title":"Identifying Clinical Skill Gaps of Healthcare Workers Using a Decision Support Algorithm in Rwanda"},"study_desc":{"title_statement":{"idno":"10.16909-dataset-55","title":"Identifying Clinical Skill Gaps of Healthcare Workers Using a Decision Support Algorithm in Rwanda","alternate_title":"ICSG-CDSA"},"authoring_entity":[{"name":"Haykel Karoui","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland"}],"production_statement":{"producers":[{"name":"Victor P. Rwandarwacu","abbr":"VR","affiliation":"Swiss Tropical and Public Health Institute in Rwanda (Swiss TPH RW)","role":"Team manager"},{"name":"Jonathan Niyonzima","abbr":"JN","affiliation":"Swiss Tropical and Public Health Institute in Rwanda (Swiss TPH RW)","role":"Data collection"},{"name":"Antoinette Makuza","abbr":"AM","affiliation":"Swiss Tropical and Public Health Institute in Rwanda (Swiss TPH RW)","role":"Data collection"},{"name":"John B. Nkuranga","abbr":"JN","affiliation":"Swiss Tropical and Public Health Institute in Rwanda (Swiss TPH RW)","role":"Data collection"},{"name":"Val\u00e9rie D\u2019Acremont","abbr":"VDA","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland and Swiss Tropical and Public Health Institute (Swiss TPH)","role":"Supervision"},{"name":"Alexandra V. Kulinkina","abbr":"AK","affiliation":"Swiss Tropical and Public Health Institute (Swiss TPH)","role":"Supervision"}],"copyright":"This dataset is available under the CC-BY licence : https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},"distribution_statement":{"contact":[{"name":"Haykel Karoui","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland","email":"haykel.karoui@unil.ch","uri":""}]},"version_statement":{"version":"Version 2.1: Edited, anonymous dataset for public distribution. Version 2.2: Update to version 2.1, enhancing the quality of the code, dataset and the README document.","version_date":"2025-02-28"},"study_info":{"keywords":[{"keyword":"Clinical Skill Gaps","vocab":"","uri":""},{"keyword":"Clinical Decision Support Algorithm (CDSA)","vocab":"","uri":""},{"keyword":"Children","vocab":"","uri":""},{"keyword":"Pediatric care","vocab":"","uri":""}],"abstract":"Digital clinical decision support algorithms (CDSAs) that guide healthcare workers during consultations can enhance adherence to guidelines and the resulting quality of care. However, this improvement depends on the accuracy of inputs (symptoms and signs) entered by healthcare workers into the digital tool, which relies mainly on their clinical skills, that are often limited, especially in resource-constrained primary care settings. This study aimed to identify and characterize potential clinical skill gaps based on CDSA data patterns and clinical observations. We retrospectively analyzed data from 20,085 pediatric consultations conducted using an IMCI-based CDSA in 16 primary health centers in Rwanda. We focused on clinical signs with numerical values: temperature, mid-upper arm circumference (MUAC), weight, height, z-scores (MUAC for age, weight for age, and weight for height), heart rate, respiratory rate and blood oxygen saturation. Statistical summary measures (frequency of skipped measurements, frequent plausible and implausible values) and their variation in individual health centers compared to the overall average were used to identify 10 health centers with irregular data patterns signaling potential clinical skill gaps. We subsequently observed 188 consultations in these health centers and interviewed healthcare workers to understand potential error causes. Observations indicated basic measurements not being assessed correctly in most children; weight (70%), MUAC (69%), temperature (67%), height (54%). These measures were predominantly conducted by minimally trained non-clinical staff in the registration area. More complex measures, done mostly by healthcare workers in the consultation room, were often skipped: respiratory rate (43%), heart rate (37%), blood oxygen saturation (33%). This was linked to underestimating the importance of these signs in child management, especially in the context of high patient loads typical at primary care level. Addressing clinical skill gaps through in-person training, eLearning and regular personalized mentoring tailored to specific health center needs is imperative to improve quality of care and enhance the benefits of CDSAs.","coll_dates":[{"start":"2021-11","end":"2022-10","cycle":""},{"start":"2022-12","end":"2023-03","cycle":""}],"nation":[{"name":"Rwanda","abbreviation":"RW"}],"geog_coverage":"16 primary healthcare centers (HCs) of Rusizi and Nyamasheke districts in Rwanda.","analysis_unit":"First dataset was collected directly by the ePOCT+ CDSA during 20,085 pediatric consultations across 16 primary health centers in Rwanda. It includes anonymized patient, healthfacility and consultation data with key clinical measurements (temperature, mid-upper arm circumference (MUAC), weight, height, MUAC for age z-score, weight for age z-score, weight for height z-score, heart rate, respiratory rate and blood oxygen saturation (SpO2).) Second dataset results from structured observations of 188 routine pediatric consultations at a subset of 10 health facilities. Clinicians used a standardized evaluation form to record clinical measurements, mirroring variables in the first dataset. This dataset is used to deepen the analysis from the primary dataset by understanding the reason for the patterns appearing from the quantitative analysis of the first dataset.","universe":"Children aged 1 day to 14 years with an acute condition, in the 16 HCs where the intervention was deployed.","data_kind":"Clinical data [cli]","notes":"First dataset: \n\t  \u2022 BC...Age.in.months...7354.categorical: Age of the child in months, categorized. \n\t  \u2022 BC...Axillary.temperature...7823: Axillary temperature of the child in degrees Celsius. \n\t  \u2022 PE215...Heart.rate..beats.per.minute....7787: Heart rate measured in beats per minute. \n\t  \u2022 VS5...Respiratory.rate..breaths.min....8469: Respiratory rate measured in breaths per minute. \n\t  \u2022 PE214...Blood.oxygen.saturation.......8385: Blood oxygen saturation percentage. \n\t  \u2022 BC...MUAC.in.cm...7833: Mid-upper arm circumference (MUAC) in centimeters. \n\t  \u2022 BC61...MUAC.for.age.z.score...7839: MUAC-for-age Z-score. \n\t  \u2022 BM1...Current.Weight..XX.X.kg....7805: Weight of the child in kilograms. \n\t  \u2022 BC7...Weight.for.age..z.score....8434: Weight-for-age Z-score. \n\t  \u2022 BM52...Height..XXX.X.cm....if.length.is.measured.subtract.0.7cm...7435: Height of the child in centimeters (0.7 cm subtracted if length was measured instead). \n\t  \u2022 BC95...Weight.for.height...7451: Weight-for-height Z-score. \n\t  \u2022 HC_code: Anonymized code for the health facility where the consultation occurred. \n\t  \n\t  Second dataset : \n\t  \u2022 T\u00b0 Assessment: Whether the temperature was assessed (assessed\/skipped). \n\t  \u2022 If skipped, why?: Reason for skipping the temperature assessment (if applicable). \n\t  \u2022 T\u00b0 Quality of Assessment: Quality rating of the temperature assessment (sufficient\/insufficient). \n\t  \u2022 If insufficient, why?: Reason for insufficient temperature assessment (if applicable). \n\t  \u2022 Extra remark about T\u00b0: Additional comments on the temperature assessment. \n\t  \u2022 MUAC Assessment: Whether the MUAC measurement was assessed (assessed\/skipped). \n\t  \u2022 If skipped, why?: Reason for skipping the MUAC assessment (if applicable). \n\t  \u2022 MUAC Quality of Assessment: Quality rating of the MUAC assessment (sufficient\/insufficient). \n\t  \u2022 If insufficient, why?: Reason for insufficient MUAC assessment (if applicable). \n\t  \u2022 Extra remark about MUAC: Additional comments on the MUAC assessment. \n\t  \u2022 Weight Assessment: Whether the weight measurement was assessed (assessed\/skipped). \n\t  \u2022 If skipped, why?: Reason for skipping the weight assessment (if applicable). \n\t  \u2022 Weight Quality of Assessment: Quality rating of the weight assessment (sufficient\/insufficient). \n\t  \u2022 If insufficient, why?: Reason for insufficient weight assessment (if applicable). \n\t  \u2022 Extra remark about Weight: Additional comments on the weight assessment. \n\t  \u2022 Height Assessment: Whether the height measurement was assessed (assessed\/skipped). \n\t  \u2022 If skipped, why?: Reason for skipping the height assessment (if applicable). \n\t  \u2022 Height Quality of Assessment: Quality rating of the height assessment (sufficient\/insufficient). \n\t  \u2022 If insufficient, why?: Reason for insufficient height assessment (if applicable). \n\t  \u2022 Extra remark about Height: Additional comments on the height assessment. \n\t  \u2022 RR Assessment: Whether the respiratory rate was assessed (assessed\/skipped). \n\t  \u2022 If skipped, why?: Reason for skipping the respiratory rate assessment (if applicable). \n\t  \u2022 RR Quality of Assessment: Quality rating of the respiratory rate assessment (sufficient\/insufficient). \n\t  \u2022 If insufficient, why?: Reason for insufficient respiratory rate assessment (if applicable). \n\t  \u2022 Extra remark about RR: Additional comments on the respiratory rate assessment. \n\t  \u2022 Sat Assessment: Whether the blood oxygen saturation was assessed (assessed\/skipped). \n\t  \u2022 If skipped, why?: Reason for skipping the saturation assessment (if applicable). \n\t  \u2022 Sat Quality of Assessment: Quality rating of the saturation assessment (sufficient\/insufficient). \n\t  \u2022 If insufficient, why?: Reason for insufficient blood oxygen saturation assessment (if applicable). \n\t  \u2022 Extra remark about Sat: Additional comments on the saturation assessment. \n\t  \u2022 HR Assessment: Whether the heart rate was assessed (assessed\/skipped). \n\t  \u2022 If skipped, why?: Reason for skipping the heart rate assessment (if applicable). \n\t  \u2022 HR Quality of Assessment: Quality rating of the heart rate assessment (sufficient\/insufficient). \n\t  \u2022 If insufficient, why?: Reason for insufficient heart rate assessment (if applicable). \n\t  \u2022 Extra remark about HR: Additional comments on the heart rate assessment. \n\t  \u2022 Remark about other SS: Any additional remarks about other signs and symptoms assessed during the consultation."},"method":{"data_collection":{"data_collectors":[{"name":"Haykel Karoui","abbr":"HK","role":"","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland"},{"name":"Jonathan Niyonzima","abbr":"JN","role":"","affiliation":"Swiss Tropical and Public Health Institute (Swiss TPH)"},{"name":"Antoinette Makuza","abbr":"AM","role":"","affiliation":"Swiss Tropical and Public Health Institute (Swiss TPH)"}],"sampling_procedure":"First dataset: \n\t\t  ePOCT+ stores all the information (date of consultation, anthropometric measures, vitals, presence\/absence of specific symptoms and signs prompted by the algorithm, diagnoses, medicines, managements, etc.) entered by the HW in the tablet during consultations. We retrospectively analyzed data from 20,085 outpatient consultations conducted between November 2021 and October 2022 with children aged 1 day to 14 years with an acute condition, in the 16 HCs where the intervention was deployed. Data cleaning, management, and analyses were conducted using R software (version 4.2.1). \n\t\t  Second dataset: \n\t\t  Based on the results of the retrospective analysis, we observed 188 routine consultations in a subset of 10 of 16 HCs (approximately 19 observations per HC), from 20 December 2022 and to 09 March 2023. The selection of HCs was guided by the retrospective analysis, ensuring that the 10 HCs chosen were those showing the most critical results. The observing study clinician obtained oral consent from the HWs and was instructed not to interfere with the consultation to avoid introducing any additional bias to the observer effect. To ensure a standardized and consistent evaluation, a digital evaluation form (Google sheets) was used. These observations were conducted over 3 days per HC, with efforts made to separate them by a few days in order to have more chance to observe several different HWs and minimize potential bias. At the end of each day of observation in a HC (and not after each consultation to avoid any influence on subsequent consultations), the observing study clinician conducted an interview with the HW to understand why the assessment of some signs was skipped.Data were exported to Microsoft Excel (Version 16.77.1) for further simple descriptive analysis.","sampling_deviation":"Second dataset: Most of the time, there was only one HW attending to children in the HC on a given day. On the rare occasions when two HW were present, each was observed by one of the two study clinicians.","coll_mode":["Other [oth]"],"research_instrument":"The second dataset for this study was derived from structured observations of 188 routine pediatric consultations conducted across a subset of 10 health facilities. Clinicians utilized a standardized evaluation form that included variables aligning with those in the first dataset. This secondary dataset was designed to provide deeper insights into patterns observed in the primary dataset through the quantitative analysis. \n\t\t\n\t\tThe data collection focused on various clinical measurements and observations, categorized as follows: \n\t\tGeneral Information: \n\t\t\u2022 Date of the consultation. \n\t\t\u2022 Health facility (coded for anonymity). \n\t\t\u2022 Clinical measurements taken at the reception and during the consultation. \n\t\t\u2022 Presence of a conducting line. Additional remarks related to the consultation. \n\t\t\n\t\tClinical Measurements: For each of the following, the dataset records whether the measurement was assessed or skipped, the quality of assessment (sufficient\/insufficient), reasons for skipping or insufficient assessments, and any extra remarks: \n\t\t\u2022 Temperature (T\u00b0). \n\t\t\u2022 MUAC (Mid-Upper Arm Circumference). \n\t\t\u2022 Weight. Height. \n\t\t\u2022 Respiratory Rate (RR). \n\t\t\u2022 Blood Oxygen Saturation (Sat). \n\t\t\u2022 Heart Rate (HR). \n\t\t\n\t\tAdditional Observations: Remarks on other signs and symptoms assessed during the consultation. The structured nature of this dataset ensures consistency in evaluating the reasons behind clinical decisions and the quality of care provided in routine pediatric consultations.","sources":[{"name":"","origin":"","characteristics":""}],"cleaning_operations":"Data editing was conducted as follows: \n\t\t**First data set:** \n\t\t\u2022 Data Extraction: \n\t\tThe dataset was extracted from the larger ePOCT+ storage system, which records all consultation-related information entered by healthcare workers (HWs) in tablets during consultations. This includes details such as the date of consultation, anthropometric measures, vital signs, the presence or absence of specific symptoms and signs prompted by the algorithm, diagnoses, medicines, and managements. \n\t\t\n\t\t\u2022 Data Cleaning: \n\t\tThe extracted data were systematically cleaned to focus solely on the variables of interest for this analysis. Irrelevant variables and incomplete records were excluded to ensure a streamlined and accurate dataset. \n\t\t\n\t\t\u2022 Anonymization: \n\t\tTo protect patient and health facilities confidentiality, the data were anonymized prior to analysis. All personal identifiers were removed, and only aggregated or coded information was retained. \n\t\t\n\t\t\u2022 Analysis Preparation: \n\t\tAfter cleaning and anonymization, the dataset was reviewed for consistency and coherence. Specific patterns of data were analyzed for the selected variables of interest, ensuring alignment with the study objectives. \n\t\t\n\t\t\u2022 Software Used: Data cleaning, management, and analyses were conducted using R software (version 4.2.1). All processes, including extraction, cleaning, and anonymization, were documented to maintain transparency and reproducibility. \n\t\t\n\t\t**Second dataset:** \n\t\t\u2022 Data Collection: Data were collected directly from respondents through a Google Forms questionnaire. The structured format ensured standardized responses across all participants, facilitating subsequent data processing and analysis. \n\t\t\n\t\t\u2022 Data Export: \n\t\tUpon completion of data collection, the dataset was exported from Google Forms to Microsoft Excel (Version 16.77.1). This provided a structured and organized format for further data handling. \n\t\t\n\t\t\u2022 Anonymization: \n\t\tAll personally identifiable information was removed during the data processing phase to protect participant confidentiality. Anonymization measures included replacing personal identifiers with unique codes and omitting any information that could reveal the identity of respondents. \n\t\t\n\t\t\u2022 Data Cleaning and Descriptive Analysis: \n\t\tThe dataset was reviewed in Microsoft Excel to ensure consistency and completeness. Responses were screened for missing or inconsistent data, and necessary corrections were made where appropriate. Simple descriptive analyses were conducted within Excel to summarize key variables and identify initial patterns in the data."}},"data_access":{"dataset_availability":{"access_place":"Unisant\u00e9 Data repository","access_place_url":"https:\/\/doi.org\/10.16909\/dataset\/55","status":"Public use files, accessible to all. Only for anonymous data"},"dataset_use":{"contact":[{"name":"RACINE, C\u00e9line (Repository manager)","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland","email":"udd.data@unisante.ch","uri":""},{"name":"Haykel Karoui","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland","email":"haykel.karoui@unil.ch","uri":""}],"cit_req":"Haykel Karoui, Identifying Clinical Skill Gaps of Healthcare Workers Using a Decision Support Algorithm in Rwanda (ICSG-CDSA-RW), 11\/2021-10\/2022, Version 2.1.","conditions":"The datasets and R scripts are available under the CC-BY licence : https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}}},"schematype":"survey","tags":[{"tag":"CC-BY"},{"tag":"Childrens"},{"tag":"Humans"}]}