But, you can plan to hope.
Toward the end of the calendar or fiscal year, the phrase “hope is not a plan” echoes through conference rooms, hallways, offices, and performance review sessions. In a world of data, analytics, predictive models, strategic plans, and business plans, there is an overwhelming (and endless) pool of data elements. The hordes of MBA’s in leadership have formalized and institutionalized decision-making based on data; success is measured by translating all outcomes into specific values. Case in point:
- Patient satisfaction measurement is based on a weighted scale. The metrics used by NCQA for patient satisfaction have been designed to correlate with a numeric value or weight as its outcome.
- Personnel are measured based on objective criteria that is aligned with a weighed numeric value.
- Resource deployment and investment (e.g. adding a new physician, deploying an ambulatory site of care) must be supported by projections and return on investment. The trend is almost always based solely on a historical average that is pushed forward versus building into the model change based on non-age and gender criteria. The introduction of disruptive technology, care delivery patterns, reimbursement, legislative or regulatory change – all impact the trend and rarely introduced into the financially driven model because it is considered soft science, aka hope.
The reality is not all bad. Healthcare and other industries historically would/have make/made decisions based on who yelled the loudest in a meeting, who nagged the longest, who carried the most political capital or weight, etc. The migration to decision making based on analytics has been a slow process with many bumps along the way.
When I joined an integrated health system in a strategy and planning function, it became evident that staff and leadership would request data for a project; yet, the decision was often already made. The purpose of the data request was to have something in the appendix or to acknowledge that a conclusion was reached with more than a gut or single-minded perspective. The analysts rarely, or barely, scoped out a problem or questioned a data request. Requests would come via telephone, email, or administrative assistant. Success was measured by how fast the data request was met. Value was measured by the alignment of the information presented with the perspective or intent of the individual making the request.
This is not a good situation.
The current environment is a cross-section of the window dressing data, pivot tables, weighed averages, and dozens of Excel worksheets. It is rare when a staff person will walk into a meeting and present a recommendation based on what they believe or feel. Perhaps their personal belief is costumed within a PowerPoint deck of charts and graphs, an attempt has been made to make the committee or board believe the recommendation was gleaned from objective data and predictive modeling.
Recently, a client contacted me and questioned the forecast for current volume of new and established visits. Their disbelief was based on the low market penetration resulting from a comparison of current volume and predicted volume. Their disregard of the model value was questioned because it could not be broken down and attributed to specific, objective, measures.
My initial reaction was to support the predictive model. Rather then question the caller, I began to probe and understand what the cause of the disbelief. This was a model, a predictive model, a forecast. It was a value based on a series of objective data points, research driven assumptions, and mathematical calculation. The value was crafted from the use of neural network technology and went through various stages of validation, tweaking, and computation.
The predictive model was a best guess – a hope.
Early in my professional like I assumed the mantle of data driven decision making. Marketing was too soft; strategic marketing was based on objective criteria. Operations based on a budget target were insufficient; a multi-year, measureable, analytic strategic business plan was the right way to go. After three decades, I find myself relying as much on experience, feeling, and hope as I do on the measures. Today, a scan of the information will create a sense of right or wrong. Hoping we will reach an objective has some level of objective criteria involved; it is still hope.
Hope is personal. It is emotion. It is in your face belief for no tangible or tangential reason or purpose. Hope comes from your heart and the experiences which have shaped who you are. Hope is messy. Hope is not black and white. Hope is an enigma, without shape, without form. Hope is grounded in the human senses very numeric values or equations.
And, hope is an emotional driver that pushes, prods, pokes, and encourages when rational, data driven actions tell you to go in a different direction. Hope can stimulate or depress. Hope creates a force that can be harnessed to overcome the greatest of barriers or the darkness of failure.
I want to hope. I want to believe that success is always possible. I want to hope for a future akin to what brings me happiness.
Planning for hope requires setting aside the measures, dashboard, the analytics at a given point in the decision making process. Planning for hope requires an emotional investment in your actions.
Hope is not a plan. Hope is that special something extra, the secret sauce, that propels a plan to success.
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