Use of tech-based information can help clients cut treatment expenses.
As the Kenyan health system matures, integration of economics into mainstream healthcare becomes necessary. In observation of the local set up, some few questions keep popping up. One of the prominent one is why patients continue paying more for a service that is readily available in the market at a lower cost.
In a study titled ‘Test Accuracy in Clinical Laboratories in Kampala’ published in the American Journal of Clinical Pathology and cited in a regional daily, the publication looked at cost variation in laboratory tests and accuracy of the results, noting large differences in prices across sampled laboratories. Strangely no evidence was observed between high prices and quality association.
An astonishing variation of up to 3,600 per cent in cost of standard tests was noted.
Economic explanations of such irrational observations may be influenced mostly by classification of the type of goods or services. However, where these cannot be explained in such a manner, an alternative hypothesis is information asymmetry between service provider and the consumers in this case patients.
A similar scenario was noted in our taxi industry in the past. Destination costs fluctuated outside normal variance curves dependent on time, day and taxi drivers. The problem really was that without a transparent costing mechanism, “guestimation” prevailed.
Enter technology and taxi apps and that is now history and any regular user of such services must have experienced the reduction in cost of fares. Could healthcare benefit from a similar approach? At the moment, about 12-hailing apps offering more than two million rides annually are in operation.
Not much has changed in terms of taxi driver’s operational costs like fuel, motor vehicle technology or maintenance costs to cause the reduction.
The only reason this has happened is because commuters have more information at their fingertips courtesy of the mobile phone. Technology has revealed cost transparency to the consumer.
Potential taxi users now know what distance they will cover, the calculated market cost per kilometre and the most efficient route the driver should take before riding.
Importantly, they can compare up to five different providers and choose on the cheapest one to take.
In the study cited earlier, it means patients paid extra in many instances for no other reason than inefficiency on the provider’s side. Ideally if there is no significant difference in quality of diagnostics, time of analysis and interpretation or results, then variation in costs should be within statistical limits.
For payers of health costs both individual and organisational, it is time we asked if a hospital’s inefficiencies or operational overheads should lead to patients paying up extra?
With a common purpose towards empowering patients to base their decisions on data driven basis, such scenarios can be gradually eliminated from the system.
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Note: The results are not exact but very close to the actual.
