Practice-Quality Variation in Low- Income Countries Jishnu Das Development Research Group The World Bank

Why are the poor sick and dying? The traditional story Poor access, poor don’t go to doctors, they visit local quacks instead Health Centre This is the best case scenario

That story was wrong Sampled village with 2 private and 1 public health care provider Village where most people go to seek care Has more than 50 health care providers

Similar Story across India

Poor people now live in villages with many health care providers to choose from and use health care providers often

Who are the providers?  80% of first-contacts (primary care) in India in private sector  77 percent of private providers in rural areas do not have medical training  Contrast: All public providers are (supposedly) trained, the majority with an MBBS 77% of providers have no degree, 18% have some other degree (BAMS, BIMS, BUMS, BHMS), and only 4% have an MBBS degree (roughly equivalent to MD in the U.S.). Average village has 3.36 providers with no degree, 0.80 providers with some degree, and 0.18 providers with an MBBS degree

Emphasis now has to move from access to access with quality Three Steps  Measure quality in many places and many ways  Understand what can work and where the problems are  Intervene and evaluate

First and most critical problem is defining the quality you want to measure

Step 1: Measuring quality  Medical Competence or Knowledge: What doctors know  Practice: What doctors do  Practice: Standardized patients: How patients are ultimately treated

Findings: Qualifications is not competence MBBS providers in Jharkhand, Bihar, and Uttar Pradesh are less competent than providers with no degrees in Gujarat and Tamil Nadu. The variation in medical education across states could be contributing to the variation in competence of MBBS providers.

What about practice?  In a place like India, the average patient interaction lasts 3 minutes, the doctor asks 3 questions and performs 1 examination (there are no nurses to take vital signs or patient histories before the patient sees the doctor)  The picture above shows doctors in the bottom, middle and top thirds of “effort” (a composite of time, questions, and exams). The bottom third spends less than 2 minutes, asks 1 question and does no examinations  Similar results in some other countries (Tanzania, Malawi, Nigeria) but not others (Paraguay)

Findings: Diagnosis of MI, SPs Das and others, 2012 There is no correlation between adherence to checklist and measures of facilities or equipment or patient load The correlation with medical training is very low Private sector doctors are more likely to adhere to the checklist

Incentives are part of it  In the public sector, the same doctor is the worst in the entire system and in his/her private clinic he/she is the best in the entire system: accountability matters.  In current work, we look at public-private differences using standardized patients and find higher rates of correct treatment and adherence to checklist in the private sector  For instance, in the case of unstable angina, correct treatment increases by close to 100 percent for similar doctors in the private relative to the public sector

Findings: Earlier study from Delhi 1.As a result, practice very different from competence; characterized by a large “know- do” gap: Providers do a lot less in practice than what they had told us they would do with a similar patient in the vignettes 2.Gap increases with competence 3.Gap consistently higher in public sector (NB: Competence essentially uncorrelated with practice in public) 4.In practice, the untrained private sector provides as good or better care than the fully trained public sector Similar results in Tanzania, Netherlands, Canada and the U.S. Private Public If providers did everything that they tell us they would do, we should observe them on the 45-degree line Instead, the private below And the public far below—in the public sector, practice is uncorrelated to knowledge!

What does this all mean  The fundamental finding thus far is that measures of quality that are frequently measured such as qualifications and availability of medical equipment are very poor predictors of quality of medical advice  Implications for regulation and policy  Measurements like standardized patients offer a biopsy of the system—providing a critical feedback loop for policy and interventions