The health care industry is often considered the epitome of evidence-based decision-making. Yet a recent New York Times Magazine article, “Making Health Care Better,” suggests that our health care system’s soaring costs and mediocre patient outcomes are due, in large part, to the fact that – like many nonprofit organizations – doctors too often put aside the scientific method, and make decisions using intuition, not data.

As the article highlights, especially in times of financial constraints, it is increasingly important to adopt an organizational culture that prioritizes investment in high-quality analytics to guide decision-making. In this newsletter, we examine what our organizations can learn from some of the challenges the health care field faces in improving patient outcomes and controlling costs.

1) Decisions about when to use certain treatments and the types of patients these treatments are appropriate for are ultimately left to a doctor’s “informed intuition”- often ignoring a large body of research data outlining the best action to take in a given situation. For example, the decision around the point at which to induce birth, or whether to perform a C-section, is often based on your doctor’s gut instinct. Yet, data, fairly conclusively, points to the best course of action.

Key strategic decisions in non-profit organizations are made typically between the board and the executive management team. With so many competing voices at the table, it is extremely easy – arguably more so than for a doctor - to slip into the trap of making decisions based on anecdote and intuition. The opinion of the individual communicating the most emotionally powerful anecdote, or that of the largest donor, will often carry sway. The key is to introduce analyzed data to the discussion not as the solution, but rather as a way to frame the discussion. Good data aggregates disparate data points and uses statistical regressions to hone in on the actions that drive to the desired outcome. Just as the analysis of vital signs collected during pregnancy provides conclusive information about the optimal procedure, so data points about your organization’s health point to the best course of action.

2) Health care’s current fee-for-service payment structure – in which doctors and hospitals are paid for the number of treatments they perform rather than patient outcomes – encourages ever more testing and treatments which ultimately leads to mounting health care costs.

Essentially, the medical industry is incenting the wrong behaviors. Many of the organizations we work with also have created perverse incentives through long-standing metrics by which they gauge their success. These metrics are often inputs instead of outcomes. For example, community federations, as part of their mission, raise money and then strategically allocate it to their direct service agencies. The agencies leverage this federation funding, along with their own fundraising, fees, and government monies, to provide services to the community. The measure of success has traditionally been total dollars raised by the federation – an input measure. In terms of consistently collecting data around impact, Federations face the same struggles that hospitals do in tracking patient outcomes. The difficulty of the process, however, does not preclude the importance of tracking such metrics. In fact, not doing so may prevent federations from allocating money in the most effective way to support their communities - potentially losing gifts from donors who do not feel the federation’s choices align with their own.

3) When doctors are confronted with data indicating that they could be doing their job better, their reaction is often to question the validity of the data and posit that their outcomes look worse compared to those of other doctors because their patients are, simply, sicker.

We encounter this situation with nearly every client. For example, a school will rationalize low enrollment and poor financials, compared to its peer group, by pointing to an especially weak local economy or demographic dips – justifying the results as beyond their ability to affect. The best retort, we’ve found, is to remind them that, “yes, you are unique…just like everybody else.” Every organization faces its unique environmental constraints. So to help our clients get at the root of the problem, when we conduct statistical analyses, we address the environmental factors that are out of the institution’s control, which typically only accounts for a minor portion of the underperformance. Still, this allows organizations to focus their attention on the issues they can actually control; in the case of the school, focusing on the quality of their education and their resulting reputation among parents. To quote the famous “Serenity Prayer,” data grants our clients “the serenity to accept the things [they] cannot change; courage to change the things [they] can; and wisdom to know the difference.”

The health care battle in Washington is currently being fought primarily using another metric – percent of Americans covered by health care, and has not yet focused heavily on improving patient outcomes while also managing costs (which, data suggests, do not have to be contradictory). That, the article suggests, will only come from helping the industry become even more data-driven. Non-profit organizations should take notice of the lessons that come from reevaluating age-old metrics and incentives. It’s a debate that hopefully will come to your executive management and board team soon. To find out how Measuring Success can help your organization define success, align incentives, and measure improvement towards outcomes, please contact me at Sacha-newsletter@measuring-success.com.

Sacha.