Are health workers prioritising the right care?

When we visualise a well-functioning healthcare system, the first thing that comes to mind is quality care, at the heart of which are skilled and motivated health workers gently guiding patients through their journey towards recovery. Today, most countries have highly trained and committed health workers. Yet, preventable complications and poor health outcomes persist, affecting patients’ quality of life and long-term health. This raises an interesting question: when training and motivation are present, why doesn’t this always translate into good clinical practice and medical care that every patient is entitled to? The answer may lie not only in how much health workers know and how driven they are, but also in what they are encouraged to prioritise in their day-to-day practice.

Why do health workers sometimes overlook essential care?

Take the case of low-income countries, where a large number of women face pregnancy-related complications every year. Common risk factors and conditions associated with high-risk pregnancies may include chronic and gestational hypertension, preeclampsia and eclampsia, severe gestational anaemia, and gestational diabetes. These [health conditions](https://link text) can be effectively managed through a range of relatively simple, low-cost measures, including regular blood pressure and glucose monitoring, timely administration of magnesium sulfate when clinically indicated and basic nutritional counselling for patients and their families. Although most health workers are aware of these measures, they may prioritise recommending additional ultrasounds, prescribing expensive micronutrient bundles, or rushing patients into C-section deliveries even when there is no genuine emergency. Part of the reason comes down to financial incentives that are not carefully designed, targeted or monitored.

How Results-Based Financing (RBF) influences health worker decisions

In a traditional Results-Based Financing (RBF) system, clinics may receive incentives for indicators such as the number of women visiting the clinic, undergoing prenatal care, complex procedures performed and medications prescribed. This can naturally encourage health workers to prioritise tasks that trigger these incentives. So, if a high-risk pregnant woman is struggling with gestational anaemia, for example, offering her low-cost nutritional counselling may receive less attention as it may be perceived as time-consuming, especially when incentives are primarily focused on the volume of patients seen. Health workers may instead proceed to write out a prescription for a high-markup imported vitamin bundle, while continuing to see other patients. This prescription would then be pushed on to clinic pharmacies for billing. If these pharmacies are poorly regulated, clinics could end up making additional profits from retail sales alongside their RBF incentives. In some cases, RBF programs may offer a reimbursement to clinics if certain basic maternal health services like routine antenatal check-ups and infection screenings are provided to patients for free. While this sounds promising, in reality, these RBF payments may be delayed by several months or sometimes more, causing clinics to face a severe cash flow crisis. In such situations, clinics may have to resort to drastic measures to keep their businesses afloat. This may cause health workers to prioritise prescribing patients expensive medications or recommending complex procedures and interventions that can bring more profit for the clinic than simpler, lower-cost alternatives.

How RBF 2.0 can improve patient outcomes and clinic performance

Although the RBF model can greatly improve the functioning of healthcare systems, it can also turn into a double-edged sword if not carefully designed and managed. To bridge these gaps and further unlock the full potential of traditional RBF, an advanced model called RBF 2.0 offers a plausible solution. Using full-scale digitisation and automation, RBF 2.0 ensures that patient records are directly integrated into the system, making it easier to keep track of patients and the services they are receiving. In the context of prioritising the right care, one of its biggest advantages includes the use of key performance indicators (KPIs) that each have a defined target. By incentivising the right treatment approach, RBF 2.0 encourages health workers to prioritise these services, resulting in better patient outcomes and clinic performance. Considering the case of high-risk pregnant women with gestational anaemia, RBF 2.0 assigns a KPI with a fixed target for providing appropriate nutritional counselling. Once a health worker meets this target by offering the desired counselling service, they are rewarded with the incentive linked to that specific service. The same principle can be applied to follow-up care to encourage health workers to prioritise not only the initial consultation or procedure but also the continued care that follows. Moreover, health workers’ incentives may vary based on how well they meet their individual targets. This means that if they perform better, they can climb up the incentive ladder. In turn, this helps ensure that high-risk pregnant women receive quality support throughout their pregnancy journey.

Automated verification and fraud prevention in healthcare payments

Now, we are also left pondering another potential problem: given the number of KPIs involved in high-risk pregnancy care, won’t unexpected errors or fraud make the verification process more difficult and potentially delay payments? Well, RBF 2.0 comprises a multi-layered verification process where each woman is contacted through an AI-powered calling system to validate the services offered to her at the clinic. Patient biometric cross-verification also forms a key part of the process as it confirms whether these women actually visited the clinic. Any fraud or discrepancy detected in the clinic records automatically triggers penalties for the clinic. These multiple verification layers therefore make the entire process simpler, faster and less prone to errors. Once the verification process is complete, the invoice and supporting documents are automatically generated and electronically signed as they pass through the approval chain. Once approved, the funds are disbursed to clinics, where a part of them is utilised to improve clinic infrastructure and purchase the latest medical equipment, while the other portion goes to health workers in the form of regular incentives that are paid out on a fixed date each month. This predictable payment schedule addresses payment delays and prevents situations where unforeseen financial pressures shift the priorities of health workers.

The way forward

Building a well-functioning healthcare system is therefore, much more than just having skilled and motivated health workers. It is also about guiding them to prioritise care that truly matters. By linking funding to well-defined, measurable aspects of care, reducing errors and ensuring incentives are regular and predictable, RBF 2.0 can reinforce traditional RBF and lay a foundation for reliable, efficient healthcare systems in the long run.