Understanding the nature of No Shows to reduce downtime

Family Plus, a non-profit professional counselling, education and advocacy for individuals and families in Greater Saint John, has served the community since 1929.  While the needs of the community and the offerings provided have changed over time, No Shows are a persistent challenge.

Background

When Heather Maughan at Family Plus was approached by the Smart & Connected Data Project team to consider potential projects, No Shows were quickly identified as a challenge they would like to address.  Unlike a cancelled appointment, No Show’s have no warning and the appointment time scheduled for the client can’t be assigned to another client.  Family Plus does not overbook their counselors, as a result, missed appointments have very real impacts including

  1. Longer wait times
  2. Lost revenue
  3. Missed opportunity to help a client

Family Plus knows that reducing No Shows will have a significant impact on their ability to deliver better results in the community.

Approach

Family Plus has data spanning many years related to clients and their appointment history.  Our team worked with Family Plus and their solution vendor to gather and evaluate data related to appointments, clients, and the services booked to uncover patterns.  The expectation was that, within this data, the team would find the drivers that predict an appointment may result in a No Show.

When the team first received the raw data from the scheduling system they worked with both Family Plus and the vendor to fully understand how the data collected represented the processes and outcomes that were observed in the day to day operations.  Matching data to business processes is a key step in any data analytics project and Family Plus was no exception.

Once the data was captured and better understood, the team set out to look for patterns in the data.  It was quickly determined that there are some critical factors that can help identify what appointments will most likely end up being missed.  The most telling factor is the manner in which the client will be paying for the session.

Early Results

Given the data evaluated, it was determined that it was possible, through evaluating a small number of factors, to predict with over 80% accuracy which appointments will be missed.  With this knowledge, Family Plus can now focus on these factors and putting processes in place to mitigate their impact on missed appointments.

In some cases, data analytics projects result in complex models that are integrated into systems which, often times, they simply result in operational process changes that are very simple to implement and very effective.  The early indications with Family Plus indicate that process changes during the intake process and the creation of interventions where higher risk appointments are scheduled may be sufficient.

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