Fifteen years ago, the world of healthcare came to grips with dilemma that inspired real change. In global health, data began to upend longstanding assumptions that shaped policy, practice, and funding. Catalogued in the 2015 book Epic Measures: One Doctor. Seven Billion Patients, physician-economist Christopher Murray rocked the global health community and ushered in a new paradigm for collecting and analyzing data on death and suffering that transformed our collective understanding of diseases like smallpox, diabetes, malaria and polio.
Murray’s work was controversial because it challenged our belief in best practices and what we knew in global health. His team uncovered better data on what was really hurting and killing people worldwide, and impacting quality of life, forcing hard conversations and long-needed change. The efforts of the global health community meant that more children in poverty were more likely to survive pediatric illness to reach age 5 – but many then died from car accidents, or other unrelated epidemics in their teens. Murray’s method for measuring the impact of health threats, what they termed Disability-Adjusted Life Years (DALYs), had the shocking result of ranking the United States No. 37 in health outcomes.
Put simply, their data showed that we weren’t often focused on funding or fighting the right things. And, in many cases, our best efforts had harmful, unintended consequences.
The rise of electronic medical records further enabled a sea change in global health. Our formerly paper-based and software-siloed system, for the first time, began to collect and connect health information in digital data stores. Strategic digitization helped researchers, hospitals, states and countries pull massive data sets together to tell new – and more comprehensive – stories about the health challenges people were facing. As these data were analyzed, studies showed that, among other things, medical and medication errors were among the leading causes of injuries and deaths in the U.S. – more so than even major diseases and workplace accidents. Some collectively ranked medical errors as the third leading cause of death in the United States.
Coming to terms with the obsolescence of long-held assumptions wasn’t easy for the global health community. Many professionals progressed through an Elisabeth-Kübler-Ross-type cycle of death and dying as they took in the studies and the resulting changes: denial, anger, bargaining, depression and acceptance. Emergent voices argued for competing approaches, and a renewed focus on fundamental safeguards. One best-selling book, The Checklist Manifesto: How to Get Things Right, made the data-driven argument that it was equally, if not more, important for the world of healthcare to focus on the fundamentals of logistics and process (e.g., washing hands and correctly placing central lines) as it was to do advance research on heart disease and cancer if they really wanted patients to live and live well.
The result has been a massive shift in the world of healthcare. Practitioners will tell you that the last 15-20 years has been a whirlwind, but a worthwhile one. Patients and practitioners have more data and information at their fingertips than ever before to not only guide healing, but also to improve wellness. Data made the case for insurance companies’ coverage of preventative medicine and procedures. Health insight is broadly shared; Fitbits tell us daily resting heart rates; and 23andme gives us genetic information that was once the stuff of science fiction.
Of course there are challenges. But few doctors or patients would want to go back to the dark ages of only 20 years ago.
Higher education is now in the throws of its own healthy move with data and innovation in practice and policy. Hard data are challenging long-held assumptions about the value of GPA or test scores in predicting student outcomes. Purpose-driven innovation is everywhere. In The End of Average, Harvard professor (and high school dropout) Todd Rose explains that absent more precise and specific data about individual students, we’re often jumping to generalities and broad swath solutions that hold the potential to cause even more challenges. He argues that we might be careening toward major policy, practice, and funding changes—even student-level outreach strategies—that are “based on everyone and relevant to no one.” We’re adopting what could be important and impactful practices, but we really don’t know, because we don’t have data to inform, instrument, tune, test, and measure the impact student-level impact of our seemingly endless stream of initiatives.
A decade or so after healthcare, higher education is entering a new era, where we have the potential to help more students experience “healthy outcomes”—learning well and finishing strong—than ever before. But we have to be willing to challenge conventional wisdom, listen to the data, so we can learn together and make the most of this transition.
This article was written by SchoolBoard from Forbes and was legally licensed through the NewsCred publisher network.