August 9, 2018

3 Critical Ways Patient Demand Forecasting Improves Patient Outcomes

2 min read

Written by: Jennifer Michelle  |  Share:

Ananya* is the Chief Nursing Officer at an academic medical center. Sitting on the couch on a beautiful Sunday afternoon, she really would have liked to be kicking back and watching Netflix, but she needed to get the nursing schedule in order.

For several months now, Ananya had been dealing with chronic nurse understaffing. It had become a perpetual frustration every time she had to deal with the schedule, and she was trying to figure out a better way.

So far, she was getting nowhere. She always staffed according to set quotas, but they still weren’t getting it right. In part, this was due to a need for new hires.

A lot of it, though, came down to getting the mix right. It just never seemed to work out the way she intended. For instance, while they – technically – had sufficient perioperative nurses on staff, 17 were on vacation this week and 23 were at CME training. She had a similar situation with the ICU and pediatric nurses, too.

Worse, they were seeing higher than average cases of sepsis in surgical patients. She had also seen an increase in readmissions.

It was her hunch that this was all related to the staffing situation and she wished she had a better way to plan for actual patient load.

Morbidity and Mortality

Numerous studies have shown a correlation between insufficient staffing levels – particularly, in regard to nurses – and patient outcomes.1 Nurse staffing levels have been associated with falls2, nosocomial infections3, and adverse perioperative outcomes. A 2007 review and meta-analysis reported a clear impact on mortality, particularly for surgical patients, showing that “increasing the care of surgical patients by 1 RN FTE per patient day would eliminate 16% of hospital-related death compared with 5.6% for medical patients.”4

Insufficient staffing levels – whether of nurses, mid-levels, or physicians – refer to both understaffing, as well as an inappropriate mix of credentials. Either way, it is always directly related to the patient load.

When a practice is able to predict patient case load a day or two in advance, and integrate that information with staff availability and credentialing, situations of understaffing or inappropriate mix drop dramatically. This leads to higher quality patient care and improved patient outcomes.


In addition to patient morbidity and mortality, preventable patient readmissions can also represent a poor outcome resulting from misaligned staffing and patient load.

A 2013 study showed an association between nursing staff levels and 30-day patient readmissions. “Each additional patient per nurse in the average nurse's workload was associated with a 7% higher odds of readmission for heart failure [odds ratio (OR)=1.07; confidence interval CI, 1.05-1.09], 6% for pneumonia patients (OR=1.06; CI, 1.03-1.09), and 9% for myocardial infarction patients (OR=1.09; CI, 1.05-1.13).”5

Clearly, patient demand forecasting can play a beneficial role in keeping patients healthier after discharge.

Patient Satisfaction

Along with reducing morbidity, mortality, and readmissions, aligning staffing levels with patient demand also has a positive impact on patient satisfaction. Reducing negative experiences, such as delays and rescheduling, improves patient perceptions of their healthcare experience. This has a ripple effect throughout the community and can make a significant impact on a practice’s reputation.

In a world where physicians and practices are rated online and negative experiences can travel via social media in the blink of an eye, the importance of patient satisfaction cannot be underestimated.

*Names and situations presented throughout this post are meant to serve as fictional examples only. They have been created as composites representing common situations, but do not reflect specific individuals or organizations.


1 AJ Lankshear, et al. “Nurse Staffing and Healthcare Outcomes: A Systematic Review of the International Research Evidence,” ANS Adv Nurs Sci. 2005 Apr-Jun;28(2):163-74.
2 Beatrice J. Kalisch, “Missed Nursing Care, Staffing, and Patient Falls,” Journal of Nursing Care Quality: January/March 2012 - Volume 27 - Issue 1 - p 6–12.,_Staffing,_and_Patient_Falls.2.aspx
3 JA Rogowski, et al. “Nurse Staffing and NICU Infection Rates,” JAMA Pediatr. 2013 May;167(5):444-50.
4 Robert L. Kane, et al, “The Association of Registered Nurse Staffing Levels and Patient Outcomes: Systematic Review and Meta-Analysis,” Medical Care • Volume 45, Number 12, December 2007, 1195-1204.
5 MD McHugh and C Ma, “Hospital Nursing and 30 Day Readmissions among Medicare Patients with Heart Failure, Acute Myocardial Infarction, and Pneumonia,” Med Care. 2013 Jan;51(1):52-9.