The Universal Test and Treat (UTT) strategy recommends that all at-risk populations screen for Human Immunodeficiency Virus (HIV) infection and individuals diagnosed as HIV positive receive early treatment irrespective of their CD4 count and clinical presentation ^{[1, 2]}. The approach prioritises HIV testing, same-day antiretroviral therapy (ART) initiation (SDI), and prompt linkage-to-care (i.e., successful completion of a first clinic visit within two weeks of an HIV diagnosis, as documented in TIER.Net) ^[3]. Even though ART treatment is freely available nationwide through the UTT strategy, challenges in linkage to HIV care persist, and a substantial proportion of patients are lost between the time of HIV diagnosis and the onset of HIV care and ART treatment ^{[4, 5]}. Poor referral systems ^[6] and knowledge gaps in clinical guideline compliance ^{[7, 8]} fuel poor health services. Gaps in linkage to care and tracking of HIV-infected individuals who missed appointments or dropped out of care remain problematic^[9] because existing patient identification systems are paper-based, limiting patient tracking. Of concern is poor data quality (i.e., data that are not sufficiently accurate, timely, credible, and complete) because the information is poorly captured partly due to insufficient supervision and mentorship in HIV data management among healthcare facility managers ^{[10, 11]}.

Compliance with standards in HIV data management processes and documentation is crucial to achieving the UNAIDS target and eradicating HIV/AIDS. Noncompliance with clinical data collection Standard Operating Procedures (SOP) leads to erroneous and misleading documentation in HIV services, fuelling low-quality services, billing discrepancies, and litigation ^[12], including morbidity and mortality rates ^[13].

Global medication error was estimated at $42 billion per year ^[14], while annual adverse events were close to 134 million, contributing to 2.6 million deaths in low and middle-income countries (LMICs) ^[14]. The National Health Service (NHS) England pays close to £98.5 million annually for avoidable erroneous prescriptions in healthcare delivery ^{[15, 16]}. The most significant errors include missed diagnoses, prescriptions, and low compliance with medical recording standards. In Indonesia, 60% of nurses evade SOPs in patient care ^[17], and Haug 2013 indicated that 67% of misdiagnoses and misinterpretations, erroneous prescriptions, and patient misidentifications are traceable to poor recording, which is often related to low compliance with approved data recording standards ^[18].

In Africa, data management inefficiencies are common, leading to disruptions in healthcare, particularly in public settings ^[19–21]. For example, data breaches generate the highest per capita costs in South Africa’s health sector. An average data breach per capita cost of $1.16 and $3.06 million was reported for 2015 and 2019, respectively ^[22]. These challenges are partly due to poor SOP compliance with established standards and guidelines for data management processes. The South African National Core Standards for Health Establishments assessment also revealed poor compliance with public service standards.

Generally, guidelines/SOPs/policies are hardly implemented as envisioned by policymakers ^{[23, 24]}. Therefore, scholars proposed that simple and robust frameworks/tools be integrated into SOP to enhance compliance and achieve the policy/SOP goals ^{[25, 26]}. A compliance framework will facilitate monitoring and minimise errors, providing motivations for SOP compliance and seamless organisation management ^[27].

SOPs are designed to provide clear and concise instructions on specific procedures. It controls and provides transparency and helps perform routine operations effectively. In addition to creating a culture of accountability by defining the roles and responsibilities of HCPs in healthcare service delivery, it serves as a reference point, minimising errors and ambiguities ^{[28, 29]}. Noncompliance with the SOP is a risk factor for serious healthcare mismanagement and litigation. It leads to missed diagnoses and prescriptions and poor billing systems ^[30]. Deviations from SOPs and disparities between SOP content and real-world implementation have been reported in developed and developing countries ^{[17, 21, 31, 32]}. About 67% of clinical data challenges are traceable to human error and poor compliance with approved standards ^[33]. The most significant errors include missed diagnoses, medication prescriptions, and low compliance with medical recording standards ^[34]. In high-income countries, about one in ten patients experience harm during hospital care ^[14]. Alhidayah (2020) suggests that only 14.7% of nurses comply with standard procedures for patient safety. In Africa, data management inefficiencies are common, leading to disruptions in healthcare, particularly in public healthcare settings ^[19].

The reasons for deviation range from a lack of resources, organisational and technical challenges, SOP inadequacy or not being user-friendly, low user capacity, complexities of applicable technologies, non-standardised data coding system, lapses in managerial oversight, and delayed decision-making to poor SOP compliance ^{[32, 35–40]}.

Generally, there is a decline in HIV epidemics worldwide, but significant work predicated on accurate and reliable data is still needed, especially among the young population. South Africa bears the highest HIV burden in the World, with approximately 7.7 million people living with HIV(PLHIV), culminating in a national prevalence of 12.7%, 16% in KwaZulu-Natal province, and 19.5% in uMgungundlovu district in 2022 ^[41–43].

Although ART is widely accessible nationwide through the UTT policy, the effectiveness of the policy is often compromised by patient attrition between HIV diagnosis and antiretroviral treatment (ART) initiation^{[4, 5]}. The reasons for these gaps include incomplete and inconsistent patient data records, poor referral systems, and knowledge gaps in clinical guidelines and compliance. Of concern are poorly captured and incomplete patient data and insufficient supervision of HIV data management processes among health facility managers ^[11], which results in poor compliance in data recording and misinterpretation of information on HIV status ^{[44] [45]}.

Frameworks are structured tools designed to help ensure compliance with organisational regulations and SOPs. They enhance adherence to SOPs and improve medical documentation and record-keeping. By clearly defining roles and responsibilities for compliance and integrating compliance controls into management processes, SOP frameworks contribute to overall compliance, ultimately improving health outcomes ^[46–48].

The South African District Health Information System is a “system that derives a combination of health statistics mainly from RHIS used in the public sector to monitor health services”^[49]. The DHMIS policy (2011) aims to guide public health information management in health facilities in the country, strengthen health system monitoring, and enhance information use in healthcare planning by standardising data collection, dissemination, and use ^[50]. It is an overarching national policy with associated processes, SOP, and data management standards in public health facilities.”TIER.Net (national HIV/ART electronic database) is embedded in the DHIS2 software and collects patient-level information at the health facility level to monitor epidemic trends and provide information for timely linkage of PLHIV to care and treatment ^[51]. The DHMIS-SOP was enacted in 2012/13 and updated in 2016 for all public health facilities and districts to guide data management nationwide ^{[28, 52]}. The SOP (DHMIS-SOP, 2016) aim to strengthen DHIS performance ^[28] by focusing on common data quality challenges and procedures, including HIV data processing. The DHMIS-SOP 2016 outlined the roles and responsibilities of HCWs in SOP use for standardising data management processes. However, after a decade of implementing the DHMIS-SOP, persistent data challenges and gaps remain a cause for concern in health services. Although HIV data processing and quality have significantly improved over the years, gaps in quality and capturing errors remain fundamental limitations, especially at the facility level ^{[53, 54]}. The literature suggests that DHMIS-SOP was implemented in only 65% of public healthcare facilities, and only 55% of HCWs were trained in SOP use ^[54]. Murphy 2013 highlighted the absence of DHMIS-SOP documents and the non-implementation in HIV data management ^[55]. Although the Western Cape Province has the most complete clinical data, within this seemingly complete dataset, notable duplicate records, accounting for 10% to 20% of entries, are reported ^[56]. The degree to which HCWs comply with the DHMIS-SOP in HIV data management processes is unknown ^[57]. Enormous resources and funding have been dedicated to HIV programmes in South Africa. These initiatives have improved the TIER.Net performance and data quality compared to other in-facility data sources. Yet, the system is not totally free of inconsistency, misclassifications, misreported data, and missing outcomes ^[53].

Generally, policies, guidelines, and SOPs are hardly implemented as envisioned by policymakers ^{[23, 24]}. Therefore, scholars have proposed simple, robust frameworks/tools to enhance implementation and compliance to achieve the policy/SOP goals ^{[25, 26]}. Integrating a compliance framework into an SOP facilitates compliance and effective monitoring, minimises errors, and aids seamless organisation management ^[58–60].

Fig 1 illustrates a causal loop diagram (CLD) with the interconnected relationships and feedback loops between system variables in compliance. It illustrates how variables influence one another, with arrows indicating the direction of impact. The circular arrows depict reinforcing (R) loops, which promote positive behaviours, and balancing loops, which can stabilise or counteract behaviours related to DHMIS-SOP compliance in HIV data management processes.

The DHMIS-SOP was enacted to strengthen data management and information use. Currently, there are concerns about poor DHMIS-SOP compliance in many facilities, with reports of poor implementation and compliance across health facilities ^{[10, 54, 55, 61]}.

The evidence of DHMIS-SOP effectiveness in improving RHIS quality is unclear, and it is difficult to ascertain whether the SOP is achieving its goals. The information, communication, and technology (ICT), data quality assurance tools within DHIS2 and integrated TB/HIV data management SOP ^{[62, 63]} have not yet yielded the expected preparedness in HIV data management processes. Few studies have referenced DHMIS-SOP on inconsistency data ^{[54, 61, 64]}. Asah (2021) compared leadership styles in the Community of Information Practice (COP) ^[64]; the MEval-SIFSA project reported shortfalls in managers’ HIS skills and information use per DHMIS-SOP ^[54]. On the other hand, Nicol (2015) identified gaps in SOP and proposed further research on organisational factors that affect the performance of RHIS^[61]. None of these studies examines the roles and responsibilities of facility managers in steering DHMIS-SOP compliance at the facility level for better data quality. The HIV data management challenges, despite the considerable investment in RHIS and HIV services, raise fundamental questions such as: Why has DHMIS-SOP not succeeded in standardising facility data? Are clinical staff adequately skilled and trained to use DHMIS-SOP? Is DHMIS-SOP user-friendly or overly complex? Are there enough resources to implement SOP? What are we not doing right? These are some of the burning questions the current study will investigate.

Thus, the proposed study will evaluate DHMIS-SOP compliance using the HIV programme as a case study and evaluate the extent to which the facility managers perform their roles and responsibilities in HIV data management processes. It will identify facilitators of /barriers to DHMIS-SOP compliance, provide valuable information on the challenges and gaps in SOP compliance at different health system levels, and provide strategies to improve compliance, thereby supporting the National Development Plan (NDP) goal of ending HIV/AIDS epidemic by 2030. Assessing the TIER.Net will reveal how well or poorly other in-facility data sources perform.