Evgeny Morozov's recent book To Save Everything, Click Here challenged "solutionism:" an orientation to social issues that prioritizes what new technology can solve, and focuses on small, algorithmically decomposable bits of wicked problems.  For example, a solutionist might think of gamified calorie counting as wonderful new way to fight obesity; a more sober analysis of the problem might lead us to think that only a very small portion of the population will find metabolic salvation in a smartphone.

Big data has been linchpin of solutionist narratives about the future of tech in health care.  However, there are still major challenges in data quality. Even if the data were perfect, causal inference still may be a challenge, as Hoffman & Podgurski explain: 

EHR vendors are making slow progress towards achieving interoperability, the ability of two or more systems to exchange information and to operate in a coordinated fashion. In 2010
only 19% of hospitals exchanged patient data with providers outside their own system. Vendors may have little incentive to produce interoperable systems because interoperability
might make it harder to market products as distinctive and easier for clinicians to switch to different EHR products if they are dissatisfied with the ones they purchased. . . . 
Even if the EHR data themselves are flawless, analysts seeking to answer causal questions, such as whether particular public health interventions have had a positive impact, will
face significant challenges relating to causal inference. These include selection bias, confounding bias, and measurement bias.
Paul Ohm adds to the data skepticism in a recent essay in the Penn L. Review.  
[A]s medical research follows the lead of Google Flu Trends and begins to slip outside these traditional institutions and their concomitant safeguards, we should be concerned about the relative lack of controls. Particularly as more medical research is conducted by profit-driven companies—
whether large corporations or small startups—we should worry about forcing the public to accept new risks to privacy with little countervailing benefit and none of the controls. The worst of all worlds would occur if medical researchers at non-profit institutions began to clamor for relaxed human subjects review in a race to the bottom to compete with their forprofit counterparts.