3. Healthcare providers are more than businesses
Data science is nothing new to health care - clinical investigators have been employing quantitative research techniques for some time. What has changed is who is using the data. It's not just clinicians and researchers anymore; many hands hold the record nowadays and for a variety of purposes. This too is nothing new, as clinical ethicist Mark Siegler lamented the decrepitude of confidentiality back in the 80s for this very reason. Hospitals and healthcare providers need to meet more than financially viable projections. They need to be places of care and support for individuals geared toward improving health on the patient's terms. Clinicians know this, but data scientist do not, and rightly so because it is not within their disciplinary training. In the world of health information technology, it is far more important that IT people be aligned with the values of healthcare than be good business people, as Paul Crotty calls for in his blog. In his considerations of the duality of man, Crotty may find that patients are far better served by a caring rather than a business response. At the same time, clinicians need to be better at leveraging IT. It has its problems but it's here to stay and clinicians need to take control of their self-regulating professions. Training is needed so that providers can use IT to deepen their relationship with patients rather than have it serve as an intermediary or justification to bill at higher rates.
While improved outcomes are primarily aspirational in the world of Big Data, there is a clear and well-worn path to increased revenue and profitability. For some this is a, if notthe primary rationale for providers to harness data science-to analyze claims data. Financial concerns have been, by far, the most important to those implementing health information technologies. While it is imperative that providers reduce duplicative or unnecessary interventions, adding the layer of cost-effectiveness for the provider creates a conflict of interest with the patient who may be more interested in her health and well-being than her provider's bottom line. The standard of care should be determined by clinical efficacy rather than profitability. Moreover, the notion that cost-effectiveness leads to a lower cost of care for patients is dubious. To the contrary, what we have seen is that efficiencies lead to greater profit and executive compensation.
4. Data Discriminates
Despite hubristic claims of a potential healthcare revolution, Big Data and its purveyors are deeply biased, discriminate based on discipline, exacerbate health literacy issues, and do not necessarily lead to changes in behavior for even the most basic things, such as provider hand hygiene. For starters, we can look at whose data is being aggregated. Underserved populations, by the very nature of their being underserved, have less data to aggregate. Minority groups, women, and other groups whose health issues have bene under-represented in research face the same if not more bias in treatment as research does not reflect their needs. We can also look to research bias and the sorts of studies that are funded and published or unpublished. For example, research conducted in areas of greater commercial value can be biased towards generating return on investment, as we have seen in numerous electronic health records. Then, there is a matter of publication bias because in too many cases only positive results are published resulting in gaps in knowledge. For example, industry-funded studies are far more likely to show clinical effectiveness of a drug than neutrally-funded studies.
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