Incentives for Accurate Diagnosis: Improving Health Care Quality in Mali

如何为发展中国家的低收入人口提供高质量的医疗保健是一项重大的政策挑战。我们最初的ESRC资助的项目发现，降低用户费用（通过免费提供初级保健）确实大大增加了马里家庭对这种保健的使用。然而，我们也发现证据表明，这种护理可能是不必要的或错误的：我们的数据表明，在政府经营的社区诊所（CSCOM）寻求护理的儿童在不需要治疗时经常被开抗疟药和抗生素。这在疟疾方面尤其突出，因为马里政府规定疟疾诊断必须通过诊断检测来确认。我们的研究结果与大量的经济学文献相一致，这些文献一方面为过度处方和过度治疗问题提供了理论基础，另一方面也证明了发展中国家公共和私营部门医生努力和护理质量的低水平。我们的执行伙伴马里卫生部表示，由于过度处方导致的方案成本增加以及需要密切监测和质量检查是扩大免费护理干预的主要障碍。我们建议开展一个后续项目，以确定过度治疗背后的主要原因，并测试替代激励机制是否可以改善护理结果，而不会产生不必要的成本。我们的分析框架是由一个“知情的专家”出售“信用好”的经济模型的动机：医生对病人的疾病和治疗的需要是无法核实的知识，病人必须购买治疗，而不知道它是否是真正的他或她需要什么。该模型阐明了医生的激励措施，患者的激励措施，诊断测试结果的可观察性，以及对测试准确性的信念如何相互作用，以产生在这种情况下的护理结果。该分析为随机对照试验（RCT）的设计提供了信息，我们将使用该试验对模型（以及过度治疗的替代理论）进行实证检验，并确定有希望改善马里公共部门护理结果的策略。我们的主要应用将是疟疾，因为这种疾病的高质量、低成本快速诊断测试是现成的。然而，考虑到我们数据中抗生素使用的惊人比率，我们还建议使用新赠款的一部分来进行额外的范围界定工作，并在可能的情况下将该项目扩大到包括细菌性疾病。RCT将在巴马科地区的48个CSCOM进行，并将使我们能够评估测试可验证性的相对重要性，提供者对诊断测试准确性的信念，以及患者对测试的教育;提供者对诊断和坚持测试结果的激励;以及患者对遵循医生建议和购买药物的激励。在随机对照试验过程中，我们将构建一个独特的数据集，捕获有关患者人口统计特征、症状和治疗结果（提供的测试和处方、购买的药物）的详细信息。我们还将进行以家庭为基础的后续调查，以获得有关患者的真实疟疾状况、治疗依从性和提供者满意度的信息。这将使我们能够估计替代激励和信息制度如何影响公共部门的过度治疗和护理结果。我们提议与马里卫生官员密切合作，以确保我们的项目产生最大的政策影响。该项目除了与马里公共卫生系统直接相关外，还将引起从事经济发展和公共卫生领域工作的研究人员和决策者的广泛兴趣。

Finding ways to deliver high-quality health care to low-income populations in developing countries is a critical policy challenge. Our initial ESRC-funded project found that reducing user fees (by providing primary health care for free) does substantially increase Malian households' use of this care. However, we also find evidence that much of this care may be unnecessary or mis-targeted: our data suggest that children seeking care in government-run community clinics (CSCOMs) are frequently prescribed antimalarials and antibiotics when they do not need the treatment. This is particularly striking for malaria, since the Malian government has mandated that malaria diagnoses be confirmed by diagnostic testing. Our findings are consistent with a large body of economic literature, which on the one hand provides theoretical underpinnings for the problem of over-prescription and over-treatment, and on the other documents low levels of doctor effort and quality of care in both the public and private sectors across the developing world. Our implementing partner, Mali Health, has indicated that the increase in program costs due to over-prescription and the need for close monitoring and quality checks are a key barrier to scaling up the free-care intervention. We propose to conduct a follow-on project to identify the leading causes behind over-treatment, and test whether alternative incentive regimes can improve care outcomes without producing unnecessary costs. Our analytical framework is motivated by economic models of an "informed expert" selling "a credence good": the doctor has knowledge about the patient's illness and need for treatment that is not verifiable, and the patient must buy the treatment without knowing if it is truly what he or she needs. The model clarifies how doctor incentives, patient incentives, observability of diagnostic test results, and beliefs about test accuracy interact to produce care outcomes in this context. This analysis informs the design of a randomized controlled trial (RCT), which we will use to empirically test the model (as well as alternative theories for over-treatment) and identify promising strategies for improving care outcomes in the Malian public sector. Our primary application will be malaria, since high-quality, low-cost rapid diagnostic tests for the disease are readily available. However, given the striking rates of antibiotic use in our data, we also propose to use part of the new grant to conduct additional scoping work and expand the project to include bacterial illness if possible.The RCT will be conducted at 48 CSCOMs in the Bamako area and will allow us to evaluate the relative importance of test verifiability, provider beliefs about diagnostic test accuracy, and patient education about testing; provider incentives to diagnose and adhere to test results; and patient incentives to follow doctor advice and purchase medications. Over the course of the RCT we will construct a unique dataset that captures detailed information about patient demographic characteristics, symptoms, and treatment outcomes (tests and prescriptions given, medications purchased). We will also conduct home-based follow-up surveys to obtain information about patients' true malaria status, compliance with treatment, and provider satisfaction. This will allow us to estimate how alternative incentive and information regimes impact over-treatment and care outcomes in the public sector. We propose to forge a close collaboration with Malian health officials, to ensure that our project has maximal policy impact. Aside from its immediate relevance for the Malian public health system, this project will be of broad interest to researchers and policymakers working in the fields of economic development and public health.