How to ensure Quality of Health Accounts (original) (raw)
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Journal of Public Health Management and Practice, 2019
Context: Leaders of government agencies are responsible for stewardship over taxpayer investments. Stewardship strengthens agency performance that is critical to improving population health. Most industries, including health care, and public enterprises, such as education, have policies for uniform data reporting and financial systems for the application of theoretical analytical techniques to organizations and entire systems. However, this is not a mainstreamed practice in local and state government public health. Program: The Public Health Uniform National Data System (PHUND$) is a financial information system for local health departments that advances the application of uniform practices to close financial analytical gaps. A 10-year retrospective overview on the development, implementation, and utility of PHUND$ is provided and supported by documented program and agency improvements to validate the analytical features and demonstrate a best practice. Results: Benefits found from utilizing PHUND$ included reducing financial risks, supporting requests for increased revenues, providing comparative analysis, isolating drivers of costs and deficits, increasing workforce financial management skills, enhancing decision-making processes, and fostering agency sustainability to support continuous improvements in quality and population health. The PHUND$ financial data definitions in the data dictionary provided the structure needed for standardized data collection and confirmed the feasibility of a standardized public health chart of accounts. Conclusion: PHUND$ analysis provided evidence on the relationship between financial and operational performance, as well as informing strategies for managing risks and improving quality. Such analysis is critical to identifying financial and operational problems and essential to mitigating financial crisis, avoiding disruption of services, and fostering agency sustainability. PHUND$ additionally serves as an instrument that can guide development of standards that measure for agency sound financial management systems. KEY WORDS: public health financial analysis, public health financial data, public health quality, chart of accounts Context System-wide adoption is needed of policies and tools to guide implementation of standardized practices for governmental public health agency financial
Finance & Management Engineering Journal of Africa , 2019
Health care planning and delivery rely heavily on data from management, administrative and clinical sources; nearly all health care activities involve gathering, analysing, or using data. Quality data can lead to quality and cost-effective health care delivery by improving patient outcomes through better decision making. Data quality is inextricably linked to the use of information systems and the health sector is increasingly an information-driven service (Hovenga et al., 1996), where information held in databases and other electronic repositories delivered in a reliable and timely manner, is critical to the health and well-being of patients, the wider population, and to the management of health care organisations (Long & Seko, 2002). Along with an increase in information complexity, there has been a parallel increase in the complex nature of organisations and organisational relationships within the health sector (Gendron & D'Onofrio, 2001).
Despite increased attention paid to health information systems and their key role for improving health systems in in Low and/or Middle income countries (LMICs), it is believed that data from majority of the health information systems contribute little to the decision-making processes due to poor data quality. We carried out an exploratory assessment of the health information system in Kenya with the main objective of determining the quality of data in terms of accuracy, timeliness and completeness. The study also considered the reasons for the observed data quality status. Data quality audits were carried out in selected health facilities. Data from the source documents at health facilities were compared to the data in the national health information systems for the same period. Key informant interviews were conducted and focus group discussions conducted during quarterly review meetings at regional levels. The study revealed that the completeness rate for the monthly reports was 86.9 percent while the timeliness of the reports was 78.7 percent. In terms of accuracy of the reports, the study showed that while there was a significant amount of low accuracy in many reports evaluated, there was a surprisingly high accuracy of reports coming from the maternity units of all health facilities visited. The accuracy of the number of deliveries could be associated with the financial incentives provided by the government to health facilities as part of the country's free maternity care project in which mothers deliver free of charge in health facilities. While most health information systems are plagued with poor data quality, a simple and practical incentive can improve accuracy, timeliness and completeness.
Factors Affecting Data Quality in the Malawian Health Management Information System
Environment and Water Resource Management / 813: Modelling and Simulation / 814: Power and Energy Systems / 815: Health Informatics, 2014
The Health Management Information System (HMIS) was introduced in Malawi with the District Health Information System (DHIS) as a tool for collecting, processing, transmitting, analysing and providing feedback of health information to various levels of the health system. Despite this effort among several others, the importance of data use in decision-making remains low and its quality is still poor. It is either incomplete, inaccurate and outdated when being reported to health managers and policy makers. The aim of this study was to determine factors that affect data quality in the HMIS in Malawi. The study was conducted in various purposively selected hospitals countrywide. These are Kamuzu Central Hospital (KCH), Bwaila, Kasungu and Ntcheu hospitals in the central region of the country. Mangochi and Balaka district hospitals were selected in southern region. Data quality was assessed by physically assessing it in registers for correctness and completeness over a period of six months to one year. Timeliness was investigated in reports that were made from health facilities to districts and finally the Health Management Unit (HMU) in the Ministry of Health (MoH) and visa versa. Semi-structured questionnaires were administered on health workers in addition to conducting focus group discussion for in-depth interview. Stakeholders were interviewed to assess the impact of feedback and the appropriate formats for feedback presentation. Patient flow and management were analysed using turnaround time and throughput as measures at Mangochi and Bwaila district hospitals to determine the efficiency and effectiveness of the health service delivery system to patients but also to determine how it affected data quality. Higher numbers of discrepancies were observed between data in registers and physical reports in comparison to HMIS. Data collectors used different standards to measure indicators, which affected the consistency of the data. These were aggravated by lack of training and supervision among data collectors. Programme managers never used HMIS data due to limited government funding. This was a major limitation in the implementation of informed decisions that could be made from HMIS. The implication of patients flow was that some data elements such as drug stocks were recorded in HMIS before actually being issued at the pharmacy, which affected correctness of the data.