Machine learning will dramatically improve health care. There are already myriad impactful ML health care applications from imaging to predicting readmissions to the back office. But there are also high-profile, expensive efforts that have not achieved their goals. In the authors’ collective roles as the CEO of a care delivery analytics business, tech-driven clinicians, and the leader of tech innovation at a major health system, they have developed and used dozens of ML applications. Many of these have succeeded, but others have not. From these experiences they have identified three common myths that exist around ML in health care.
Machine learning will dramatically improve health care. There are already a myriad impactful ML health care applications from imaging to predicting readmissions to the back office. But there are also high-profile, expensive efforts that have not achieved their goals.
In our collective roles as the CEO of a care delivery analytics business, tech-driven clinicians, and the leader of tech innovation at a major health system, we have developed and used dozens of ML applications. Many of these have succeeded, but others have not. From these experiences we have identified three common myths that exist around ML in health care.
The reality is that ML applications can perform some of what doctors do today, but they will not replace most of what doctors do in the foreseeable future (even radiologists). Doctors perform three main duties: (1) help prevent people from getting sick, (2) diagnose what’s wrong when people do, and (3) and then provide care and treatment. ML does have an important contribution to make with the first and second of these functions. For example, ML algorithms have proven especially useful in predicting cancer characteristics from imaging or in diagnosing fractures from x-rays. Unsupervised learning algorithms have demonstrated potential in linking disease risks to genomic biomarkers.
However, even with the further development of these applications, they will not replicate a doctor’s ability to provide care and treatment. The ML output still must be analyzed by someone with domain knowledge; otherwise, trivial data may be interpreted as essential and essential data as trivial. These relationships then have to be translated to actionable clinical management.
There is also a human element in helping patients decide whether and in what way to receive treatment. Patients often have concerns or apprehensions about undergoing treatment. Doctors need to incorporate the patient’s mental state, expectations, past history, and cultural factors into shared decision making with the patient and their family. Patients appreciate this human interaction and not receiving it at sensitive times may be upsetting.
Finally, once the treatment is completed, the recovery process itself requires close monitoring and care. Complications are often detected through clinical observation as opposed to protocol-driven testing or diagnostics.
The reality is that they are necessary but not sufficient. More data is better, but only if it is the right data and we fully understand it. We find it helpful to ask the following questions:
How was the data gathered? Consider how the adoption of electronic health records (EHRs) could lead to all the diagnoses made and medications prescribed by different physicians for a patient to be captured in a single record — one that would be more comprehensive than individual physicians’ paper records. Without taking into account this change — which would reduce, if not eliminate, information falling through the cracks — one might wrongly conclude that all of a sudden, patients got sicker.
For what purpose was the data gathered? Consider lab data collected by a hospital.
