A Cognitive Solution to the Problem of No-Shows
The problem of patient no-shows is a vexing one that spans the care spectrum. These events include instances where patients fail to attend an appointment, arrive too late for an appointment, or cancel an appointment on too short of a notice.1 Estimates are that anywhere from 14-50% of primary care appointments are no-shows.1
The impacts of missed appointments are substantial. For providers, no-shows have a direct hit to revenues with total dollar impacts ranging by specialty. They also contribute to decreased provider satisfaction and wasted time.2 For patients, the consequences can be dire. Missed appointments can lead to poor chronic condition management, a lack of preventative care, and the increased possibility of an avoidable hospital visit.1
A disproportionate segment of the patient population tends to drive the largest percentage of no-shows. In one study conducted at the Cleveland Clinic, 1.5% of patients accounted for 20% of all missed appointments.3 Following the 80/20 rule that seems to hold true for so much of healthcare, the outlier patients that make up 20% of the population tend to drive most of the no-shows and, consequently, have the biggest cost impact to provider operations.
There are some shared characteristics associated with a higher risk of a no-show appointment. These span clinical and non-clinical attributes including a history of no-shows, age, ethnicity, socioeconomic status, behavioral health conditions, and co-morbidities.1 Patient perception also plays a significant role in attendance. Emotions, perceived disrespect, and a lack of education around the scheduling system have all been linked to the likelihood of a no-show instance.4
Jvion’s Cognitive Machine is being used by providers to help not only identify potential no-shows, but to determine the best possible action that will have the biggest potential impact on driving patient attendance. The machine, which uses an Eigen-based backbone, delivers:
The patients at-risk of missing an appointment, showing up late, or cancelling within a specified timeframe
The factors influencing that risk of cancelation including the clinical and non-clinical drivers such as lack of access to transportation, depression, and language barriers
Insight into which patients can be impacted and the specific actions that will reduce the risk of a no-show appointment
With the Jvion machine, providers know when overbooking appointments is the best strategy to mitigate potential operational losses and when actions including reminders, patient transportation, and education will drive a patient to attend his or her appointment. Jvion’s Cognitive Machine is helping providers better manage patient appointment scheduling to reduce losses and optimize practice resource utilization. But the most important outcome of this vector is the impact that it has on patient care. The solution is helping ensure the best possible outcome for patients. By knowing how to engage a person so that they make it to an appointment can reduce the risk of complications and the deterioration that leads to preventable stays in the hospital and avoidable pain and suffering.3
 T. Gebhart, "No-Show Management in Primary Care: A Quality Improvement Project," Sholar Archive, p. 28, 13 April 2017.  P. Bjorn Berg, B. Michael Murr, M. David Chermak, M. Jonathan Woodall, M. M. Michael Pignone, M. M. Robert S. Sandler and P. Brian Denton, "Estimating the Cost of No-shows and Evaluating the Effectos of Mitigation Strategies," Medical Decision Making, vol. 33, no. 8, pp. 976-985, 2013.  M. Fred DeGrandis, L. Hultine and M. Craig Nielsen, Reducing No-Shows to Enhance Patient Access and Provider Satisfaction, ACHE Managment Innovations Poster Session, 2016.  P. Naomi L. Lacy, M. M. Audrey Paulman, B. Matthew D. Reuter and M. A. F. Bruce Lovejoy, "Why We Don't Come: Patient Perceptions on No-Shows," The Annals of Family Medicine, vol. 2, no. 6, pp. 541-545, 2004.
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