How to build an efficient, effective forcing function
Health systems can get hamstrung by long decision-making timelines. Here’s how to incentivize acceleration in the process.
In reading parts 1 and 2 of this series, readers have witnessed the problem, observing the slow decision pace and the burden it places on the health system.
They also have made a diagnosis, recognizing the structural cause – the lack of an effective internal forcing function that accelerates decision pace before optionality is lost.
If this is your entry point to the series, part 1 examined why health systems fail to act on what their data already shows, and part 2 identified the structural cause and governance tools available to address it.
The data professional is positioned to ask the vital questions necessary to open the governance conversation at the right level to effect change. First, an honest assessment is needed to determine where the governance structure currently stands. Three measurements provide the unvarnished picture.
Decision latency. How long does it take for a signal visible in the data to reach governance-level discussion and resolution? Record the date when the trend in the data is first observable. How long before it first appears on the leadership agenda? How long before it is resolved? The gap is decision latency. More likely, this gap is never measured – its magnitude could be shocking. As in the case of the $50 million opportunity presented in part 1, you may find no resolution at all - the issue is never resolved – an infinite decision gap, or latency.
Governance backlog. Review leadership agendas over the past one to two years. How many decisions appear on these agendas more than once without resolution? Is chronic reoccurrence the norm rather than the exception? Date the first appearance of each item and track the number of discussions without closure. This is the governance backlog – it’s as real, measurable and costly as any operational backlog. It's curious to see how many decisions shared the leadership agenda with the $50 million opportunity – each one contributing to the team’s anxiety and handcuffing its ability to prioritize action.
Analytical yield. Tally the hours spent by analytical staff over the past year in support of the leadership governance agenda. What proportion resulted in a resolved governance decision? How much time was spent without resolution? The answer is not a reflection of the analytic staff. It is a measurement of governance structure failure – something that leadership needs to see.
Before building an internal forcing function, leaders must understand where time is currently being lost. These three measures do not evaluate organizational performance. They diagnose the health of the governance process itself.
Three questions to open the conversation
Opening the governance performance question is not for the faint of heart. The approach must be constructive rather than confrontational. The following three questions open the dialogue with the leadership in such a manner.
“What is our decision latency? How long does it typically take for something visible in the data to reach the governance level and be resolved?” The question is not rhetorical and should not be answered generically. Instead, it is analytical, non-threatening and demands a data-driven answer. Using the $50 million opportunity from Part 1, surfacing the weeks or months that have transpired since the margin decline commenced – while the decision (or indecision) on the opportunity remains unresolved – highlights the governance lag in measurable terms. It can no longer act as a cultural observation that can be discounted.
“For the five most consequential pending decisions, what data would we need to see before resolving them, and are we currently producing it on the right timeline?” This question does two things simultaneously – it shifts the discussion from reactive to strategic – from producing data that leadership requests to data which decisions require. Furthermore, it forces a distinction that most governance conversations never make explicitly – is this decision waiting because the data are insufficient, or because the governance structure has no mechanism for closure? These are fundamentally different problems with different solutions. A data gap requires better analysis. A governance gap requires a forcing function. Conflating them – which organizations do routinely – enables the governance gap to hide behind the appearance of analytical diligence.
Confirming in advance exactly which data a decision requires also removes a common deferral mechanism. After the data standard is agreed upon, the request for “additional information” loses its power to delay. Applied to the $50 million opportunity introduced in part 1 – a comparative pro forma model of the system’s margin trajectory with and without an affirmative decision, combined with an inventory of projects consuming the same resources, would have defined the decision boundary clearly. Further delay would have been exposed for what it was – not prudence, but avoidance.
“What would it look like to build a formal escalation pathway for data signals that meet a specific threshold of concern?” How does the organization introduce the Red Flag concept? First, leadership must recognize the hidden cost of delayed signals and that early red flags must be privileged, even if they later prove incorrect. One CEO describes this as “executive courage” – the willingness to surface concerns early while everyone else is riding high with confidence. This CEO actively encourages his team to “look for ghosts” and to address issues while small and readily contained.
For the data professional, building the escalation pathway is the structural counterpart to the cultural aspiration – it makes executive courage the expected behavior rather than the exceptional one.
Connecting data governance to board governance
ACHDM Fellows operate naturally within data governance frameworks – specifically, the RACI model (responsibility, accountability, consultation, information). These frameworks – structures that define who owns the decision, who has data input rights, and who is informed. They also reflect what was identified in part 2 as missing from critical governance decisions in health systems and slowing decision pace.
We are not suggesting that data professionals overreach their role by assuming governance responsibility. Instead, as practitioners deeply rooted in these frameworks, they possess the knowhow to lend their expertise to solving the vexing governance problem.
A health system with an effective internal forcing function stands out among its peers. An experienced data professional will recognize it immediately.
Analytical yield increases. A greater percentage of reports and analyses result in a resolved issue. Correspondingly, fewer decisions are deferred awaiting further study.
Decision latency decreases. Decisions are reached earlier, while optionality still exists. The time lag, from when the data first signals a trend to when the issue is resolved at the governance level, is noticeably lessened.
Early signal escalation increases. How often are issues raised early, before sufficient evidence exists yet, while time remains to investigate, course-correct and limit consequence? Are those that raise the red flags acknowledged or vilified, regardless of whether the warning proved true?
Compensation and recognition alignment. Decision pace metrics appear explicitly in leadership compensation structures, not buried inside generic “leadership” or “strategic execution” components. Equally important, the people who raise early, incomplete concerns are recognized for doing so – regardless of whether the concern ultimately proved correct – while chronic silence is treated as a career and compensation inhibitor. The individual or committee that owns a decision has a personal financial and career progression stake in whether the decision closes on time.
Each of these is readily observable, if not quantifiable. Tracking them becomes a forcing function. It further makes governance effectiveness visible in much the same way the mission-margin map makes mission and financial viability explicit.
The optimist’s conclusion
The argument posited by these three articles is both structural and unflinching. Too many health systems lack an effective internal forcing function. The result is costly delay and a societal burden that is no longer tolerable.
The $50 million that didn’t get saved in part 1 is not an isolated failure. It is a predictable and recurring outcome of a governance architecture without a mechanism to price delay. This absence is not a sign of poor leadership but of poor structure.
Fortunately, structural problems have structural solutions. Data professionals bring unique discipline to this conversation by measuring decision latency and analytical yield and promoting early signals. They assist leadership in embedding RACI principles in governance practices and build the analytical infrastructure to make governance effectiveness visible. The reward is an organization that will have choices when choices still matter.
Every organization measures quality. Every organization measures finance. Every organization measures operations. Few measure the time between knowing and deciding. Until they do, the cost of delay will remain invisible. The data are never the problem. The question is whether governance is prepared to govern before time governs the organization.
Mark A. Van Sumeren is a Fellow of the American College of Health Data Management and the author of Strategic Leadership When Time Is the Constraint.