Is the routine health information system ready to support the planned national health insurance scheme in South Africa?

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Study Justification:
The study aimed to assess the readiness of the routine health information system in South Africa to support the implementation of the planned National Health Insurance (NHI) scheme. The NHI requires a reliable and standardized health information system that can support reimbursements and resource management through Diagnosis-Related Groupers. The study aimed to identify any gaps or challenges in the current system that may hinder its ability to support the NHI.
Highlights:
– The study reviewed inpatient health records from 45 representative public hospitals in 10 NHI pilot districts in South Africa.
– Data from 5795 inpatient health records were analyzed, focusing on 10 pre-defined data elements relevant to NHI reimbursements.
– The study found that less than 15% of diagnoses were coded using ICD-10 codes, indicating a lack of standardized coding.
– Inconsistencies were observed between registers, patient folders, and discharge summaries for important data elements such as attending physician’s signature, investigation results, patient’s age, and discharge diagnosis.
– The absence of coded diagnoses and data inaccuracies suggest that the current routine health information systems are not ready to support reimbursements and resource management for the NHI.
Recommendations:
– Institutional capacity should be developed to undertake diagnostic coding and improve data quality.
– Standard discharge summaries should be completed for every inpatient to ensure accurate and comprehensive information.
– Training and support should be provided to healthcare providers to ensure proper documentation and data entry.
– Regular audits and quality checks should be conducted to monitor the accuracy and completeness of health records.
– Investment in health information technology infrastructure and systems should be prioritized to support the NHI implementation.
Key Role Players:
– South African National Department of Health (NDoH)
– Provincial and district health departments
– Hospital CEOs and managers
– Healthcare providers (doctors, nurses, etc.)
– Health information management professionals
– Health information technology experts
Cost Items for Planning Recommendations:
– Training and capacity building programs for healthcare providers and health information management professionals.
– Development and implementation of standardized coding systems and tools.
– Upgrading and maintenance of health information technology infrastructure.
– Regular audits and quality assurance processes.
– Research and evaluation to monitor the effectiveness of the implemented recommendations.

The strength of evidence for this abstract is 6 out of 10.
The evidence in the abstract is moderately strong, but there are areas for improvement. The study design is cross-sectional and includes a representative sample of public hospitals in 10 NHI pilot districts, which enhances the generalizability of the findings. The study assesses the quality of inpatient health records and the congruence between different sources of information. However, the abstract does not provide information on the specific methods used to assess quality and congruence, which limits the ability to evaluate the rigor of the study. Additionally, the abstract does not mention any statistical analyses conducted to support the findings. To improve the strength of the evidence, the abstract should provide more details on the methods used, including the specific criteria for assessing quality and congruence, and describe the statistical analyses performed. This would allow readers to better evaluate the validity and reliability of the findings.

Implementation of a National Health Insurance (NHI) in South Africa requires a reliable, standardized health information system that supports Diagnosis-Related Groupers for reimbursements and resource management. We assessed the quality of inpatient health records, the availability of standard discharge summaries and coded clinical data and the congruence between inpatient health records and discharge summaries in public-sector hospitals to support the NHI implementation in terms of reimbursement and resource management. We undertook a cross-sectional health-records review from 45 representative public hospitals consisting of seven tertiary, 10 regional and 28 district hospitals in 10 NHI pilot districts representing all nine provinces. Data were abstracted from a randomly selected sample of 5795 inpatient health records from the surgical, medical, obstetrics and gynaecology, paediatrics and psychiatry departments. Quality was assessed for 10 pre-defined data elements relevant to NHI reimbursements, by comparing information in source registers, patient folders and discharge summaries for patients admitted in March and July 2015. Cohen’s/Fleiss’ kappa coefficients (κ) were used to measure agreements between the sources. While 3768 (65%) of the 5795 inpatient-level records contained a discharge summary, less than 835 (15%) of diagnoses were coded using ICD-10 codes. Despite most of the records having correct patient identifiers [κ: 0.92; 95% confidence interval (CI) 0.91-0.93], significant inconsistencies were observed between the registers, patient folders and discharge summaries for some data elements: attending physician’s signature (κ: 0.71; 95% CI 0.67-0.75); results of the investigation (κ: 0.71; 95% CI 0.69-0.74); patient’s age (κ: 0.72; 95% CI 0.70-0.74); and discharge diagnosis (κ: 0.92; 95% CI 0.90-0.94). The strength of agreement for all elements was statistically significant (P-value ≤ 0.001). The absence of coded inpatient diagnoses and identified data inaccuracies indicates that existing routine health information systems in public-sector hospitals in the NHI pilot districts are not yet able to sufficiently support reimbursements and resource management. Institutional capacity is needed to undertake diagnostic coding, improve data quality and ensure that a standard discharge summary is completed for every inpatient.

A sample of public-sector hospitals across 10 NHI pilot districts selected by the South African National Department of Health (NDoH), was identified (Figure 1). These districts were selected based on a combination of factors such as demographics, socio-economic factors including income levels and social determinants of health, health profiles, health-delivery performance, health-service management, financial and resource management (Matsoso and Fryatt, 2013). NHI pilot districts. 1. OR Tambo; 2. Thabo Mofutsanyana; 3. City of Tshwane; 4. uMzinyathi; 5. uMgungundlovu; 6. Vhembe; 7. Gert Sibande; 8. Pixley ka Seme; 9. Dr Kenneth Kaurnda; 10. Eden. A retrospective cross-sectional health records review was undertaken on a sample of public-sector hospitals in South Africa, with a focus on addressing all health-systems bottlenecks and challenges to reverse the worsening disease burden. The NHI pilot sites were established to test the feasibility of implementing the NHI to reduce the high maternal and child mortality rates in South Africa and other components of the disease burden. The objectives of the pilots include testing the ability of the districts to assume greater responsibilities under the NHI, to assess utilization patterns, and costs and affordability of implementing a primary health-care service package (Matsoso and Fryatt, 2013). The sampling frame for the study was all public hospitals with their five treatment departments—surgical, medical, paediatrics, obstetrics and gynaecology and psychiatry, located within the 10 NHI pilot districts (N = 83). A cluster study design was adopted whereby each of the NHI pilot districts was considered a cluster. To cover each stratum (i.e. hospital level), proportional sampling was used to randomly select three district hospitals, and all regional and tertiary hospitals from each of the pilot districts in the nine provinces to yield a total of 45 hospitals. Table 1 outlines the breakdown of the sampled hospitals. The sample size of in-patient health records was determined by assuming a 50% prevalence for the number of admissions per hospital, with a 95% confidence level and a precision level of 0.05. Given the cluster design of the study and unknown effect on the data, a design effect of 1.5 was assumed. Based on these parameters, a sample of 578 in-patient records was estimated for each district. Characteristics of the different hospital levels (South African National Department of Health, 2004) Consequently, data were expected from 5780 routine in-patient-level records at 45 sampled public-sector hospitals from five treatment departments or groups of departments—surgical, medical, paediatrics, obstetrics and gynaecology and psychiatry. For consistency across the hospitals, all departments in each hospital were assigned to one of these five groups. The records were drawn proportionally based on the estimated number of admissions in the selected hospitals during the study months to allow for seasonal disease surges (March 2015, summer season and a peak for diarrhoeal cases, and July 2015, winter period) and the number of hospitals per level in each NHI pilot district (Table 2). Estimated numbers of records for review by types of public hospitals within NHI pilot districts Depending on the size of the hospital, approximately 10 records were accessed from each of the treatment departments for each study month, at each study hospital. All records were accessed if the number of admissions during a study month in a department was <10. Data were collected between August 2016 and April 2019 by trained fieldworkers. Research teams were given log sheets to be signed by the managers (CEOs) of the hospitals visited. The log sheet included the names of hospitals visited, time spent at the facility and date of visit. Also, the teams were given a data-collection summary checklist (Supplementary Appendix SA) outlining the data-collection activities conducted in each hospital. These included extracting and photographing information from selected in-patient health records contained in registers, patient folders and discharge summaries and extracting information from available eRHIS (electronic RHIS). This information was captured using the Research Electronic Data Capture (REDCap), a web-based application for building and managing online surveys and databases (Harris et al., 2009). The project manager reviewed the completed instruments and the data-collection summary checklist and communicated any inconsistencies to the supervisors/fieldworkers to resolve any data-quality problems that occurred during fieldwork. Document and documentation standards and the availability of discharge summaries were investigated using a data-collection checklist (Supplementary Appendix SA). This checklist was used to identify the relevant documents and information on the availability of patient-discharge summaries. The presence of a patient-discharge summary was confirmed by taking a de-identified photo of the record and uploading it onto REDCap. Record quality was measured using two dimensions: (1) Completeness of the data in the ward register, patient medical record and discharge summaries; and (2) data accuracy i.e. the agreement between data in the patient medical record (paper-based and electronic), discharge summaries and ward register for 10 pre-defined data elements: patient age, patient identifier, attending physician’s signature, admission diagnosis, discharge date, discharge (final) diagnosis, condition on discharge, procedures, follow-up plan and results of the investigation (Wimsett et al., 2014). Data completeness was assessed by reviewing the proportion of discharge summaries that had all the required data fields completed by a clinical registrar, a general practitioner/medical officer or nursing staff. A percentage average of the availability of coded diagnoses during the two 1-month periods was reviewed. Record accuracy was investigated at two levels by measuring the agreement analysis for the 10 pre-defined data elements: Statistical analyses were completed using the svyset command in STATA 16.0 (StataCorp LLC,) to incorporate the three-stage cluster study design of the sample. For the first stage, the primary sampling unit was the hospital/facility stratified by the hospital/facility type and there was no finite population correction since the number of records to be reviewed was not known beforehand. The second and the third stages only had the study month and the facility departments as the primary sampling units, respectively. Once set, proportions for the documents and the documentation standards were estimated and reported with their respective 95% confidence intervals (CIs). Cohen's and Fleiss’ kappa coefficients (κ) were used to measure intra-rater reliability between the values of the pre-defined data elements found in the discharge summaries compared to the patient’s health records and ward registers, ignoring the survey design. Cohen’s kappa was used for the 10 data elements for the two data sources—patients’ folder vs. discharge summaries and, where ward registers were included in the comparison, Fleiss’ kappa was used. We reported the measure of agreement/kappa scores and the CI ranges. A P-value of <0.05 eliminated chance agreement and CIs were also reported. The study proposal received ethics clearance from the Human Research Ethics Committee of the South African Medical Research Council (Ref: EC 003-2/2016) and the University of Pretoria Health Ethics Committee (Ref No: 305/2017). Permission to access the patient’s health records from the various hospitals was obtained from the respective provincial and district health departments, and the study hospitals. Because the study did not require direct interactions with patients, patient consent was not required. However, strict confidentiality was adhered to with regard to the protection of information obtained from patient records; individual patient health records were de-identified and assigned a unique subject identifier at the point of data collection. The record of the links between project ID codes (unique subject identifier) and the patient identifiers (folder numbers) was securely kept in an encrypted database (REDCap).

Based on the provided information, here are some potential innovations that can be used to improve access to maternal health:

1. Electronic Health Records (EHR): Implementing a standardized electronic health records system can improve the accuracy and accessibility of patient information. This would allow healthcare providers to easily access and update patient records, leading to more efficient and coordinated care.

2. Diagnostic Coding System: Developing a comprehensive diagnostic coding system, such as the International Classification of Diseases (ICD) codes, can help standardize the recording and reporting of diagnoses. This would facilitate data analysis, resource allocation, and reimbursement processes.

3. Improved Data Quality Assurance: Establishing mechanisms to ensure the accuracy and completeness of health data, such as regular audits and training programs for healthcare providers, can enhance the reliability of health information. This would enable better decision-making and resource management.

4. Mobile Health (mHealth) Solutions: Utilizing mobile technologies, such as mobile apps and SMS reminders, can help improve access to maternal health information and services. These solutions can provide timely reminders for prenatal care appointments, offer educational resources, and facilitate communication between healthcare providers and pregnant women.

5. Telemedicine: Implementing telemedicine services, including virtual consultations and remote monitoring, can enhance access to maternal healthcare, especially in remote or underserved areas. This would enable pregnant women to receive medical advice and support without the need for physical travel.

6. Community Health Workers: Training and deploying community health workers who can provide basic maternal health services, education, and support in local communities can help bridge the gap in access to healthcare, particularly in rural or marginalized areas.

7. Public-Private Partnerships: Collaborating with private healthcare providers and organizations can help expand access to maternal health services. This can involve leveraging private sector resources, expertise, and infrastructure to improve service delivery and reach more pregnant women.

It is important to note that the specific context and needs of South Africa should be considered when implementing these innovations.
AI Innovations Description
Based on the provided description, the recommendation to improve access to maternal health would be to enhance the routine health information system in public-sector hospitals in South Africa. This can be achieved through the following steps:

1. Implement standardized discharge summaries: Ensure that a standard discharge summary is completed for every inpatient. This will improve the quality and consistency of information recorded and shared between healthcare providers.

2. Improve diagnostic coding: Establish institutional capacity to undertake diagnostic coding using ICD-10 codes. This will enable accurate and standardized recording of diagnoses, which is essential for reimbursement and resource management under the National Health Insurance (NHI) scheme.

3. Enhance data quality: Address identified data inaccuracies by improving the completeness and accuracy of data elements such as attending physician’s signature, investigation results, patient’s age, and discharge diagnosis. This can be achieved through training and quality assurance measures.

4. Strengthen health information systems: Invest in the necessary infrastructure, technology, and human resources to support a reliable and standardized health information system. This includes electronic health records (eRHIS) and data capture tools like REDCap.

5. Ensure compliance with documentation standards: Regularly assess and monitor the adherence to document and documentation standards to maintain data integrity and consistency across hospitals.

By implementing these recommendations, the routine health information system in public-sector hospitals can be enhanced to better support the planned National Health Insurance scheme in South Africa, ultimately improving access to maternal health services.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Strengthen Routine Health Information Systems: Enhance the existing health information systems in public-sector hospitals to ensure they are reliable, standardized, and capable of supporting the National Health Insurance (NHI) scheme. This includes improving the quality of inpatient health records, ensuring the availability of standard discharge summaries, and promoting the use of coded clinical data.

2. Diagnostic Coding: Implement a system for diagnostic coding using ICD-10 codes to accurately capture and categorize diagnoses. This will facilitate reimbursements and resource management under the NHI scheme.

3. Data Quality Improvement: Develop strategies to improve the accuracy and completeness of data in health records, discharge summaries, and registers. This may involve training healthcare providers on proper documentation practices and implementing quality control measures.

4. Standardized Discharge Summaries: Ensure that a standard discharge summary is completed for every inpatient, providing comprehensive information about the patient’s condition, diagnosis, treatment, and follow-up plan. This will support continuity of care and enable effective resource management.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define Key Performance Indicators (KPIs): Identify specific indicators that reflect access to maternal health, such as the number of maternal health visits, the percentage of pregnant women receiving prenatal care, or the rate of maternal mortality. These KPIs will serve as measurable outcomes to assess the impact of the recommendations.

2. Baseline Data Collection: Collect baseline data on the selected KPIs before implementing the recommendations. This will provide a benchmark for comparison and help establish the current state of access to maternal health.

3. Intervention Implementation: Implement the recommended interventions, such as strengthening health information systems, introducing diagnostic coding, improving data quality, and standardizing discharge summaries. Ensure proper training and support for healthcare providers during the implementation process.

4. Data Collection Post-Intervention: After the interventions have been implemented, collect data on the selected KPIs using the same methodology as the baseline data collection. This will allow for a comparison between the pre- and post-intervention periods.

5. Data Analysis: Analyze the collected data to assess the impact of the recommendations on access to maternal health. Compare the KPIs before and after the interventions to determine any improvements or changes. Statistical methods, such as regression analysis or t-tests, can be used to evaluate the significance of the findings.

6. Interpretation and Reporting: Interpret the results of the data analysis and report the findings. Highlight any improvements in access to maternal health resulting from the implemented interventions. Provide recommendations for further improvements or adjustments based on the findings.

By following this methodology, it will be possible to simulate the impact of the recommendations on improving access to maternal health and assess the effectiveness of the interventions in supporting the planned national health insurance scheme in South Africa.

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