BUSI 611 Week 2 Discussion Board Forum 2 Example

Discussion Board Forum 2: Decision Making and Forecasting in Health Care Operations

BUSI 611 Operations Management for Health Organizations, Discussion Board Forum 2

Thread by [Student Name]

The hospital, its pediatric unit, its data and the classmates are composites; the textbook, Scripture and the peer-reviewed sources cited are real.

In November 2022, our 180-bed community hospital's 14-bed pediatric unit filled in three days. Respiratory syncytial virus, influenza and other viruses arrived together and early, and the regional children's hospital, 70 miles away, stopped accepting most transfers. Our emergency department held children with low oxygen levels on adult stretchers for up to two days while nurses who rarely care for infants managed high-flow oxygen. Parents waited in hallways, and two families drove their children to another state on their own. Leaders later agreed that the surge was foreseeable in general, since respiratory seasons come every year, but that the hospital had no forecast, no early warning and no plan for when to open extra beds, call in pediatric nurses or postpone elective pediatric surgery.

Langabeer and Helton (2016) describe forecasting as estimating future demand from patterns in past data, such as trend and seasonality, and they stress that every forecast carries error that managers must state and plan around. They also treat decision making as a structured process in which managers define options and choose criteria before the pressure arrives. For our pediatric unit, those ideas point to two missing pieces. We had years of admissions data showing a winter peak but never turned it into a weekly forecast with an error range, and we had never decided in advance what we would do at 80%, 90% or 100% occupancy. In the surge, every decision was made at 2 a.m. by whoever was in charge.

The 2022 surge shows how quickly demand can move. Cleary et al. (2026) used Oregon's statewide capacity system and discharge data to describe the state's response to that year's RSV epidemic. Median statewide pediatric inpatient census rose 19%, from 306 to 364, and pediatric intensive care census rose 50%, from 24 to 36. Children's hospitals cut weekly elective pediatric admissions by 33%, and admissions from community hospital emergency departments to community hospitals rose 160%, from 15 to 39 a week, as smaller hospitals kept children they would normally have transferred. In other words, the surge landed on hospitals like ours whether we planned for it or not.

Forecasting will not predict such a season perfectly, but it helps. Shah et al. (2021) tested widely used automated forecasting methods on daily nonelective bed occupancy at 121 English hospital trusts from 2011 to 2017. Winter forecasts were less accurate than summer ones, and error grew the further ahead the models looked, but exponential smoothing produced a median error of 4.91% over four winter weeks, against 8.5% for the older recommended method. Simple, automated models built from a hospital's own data outperformed rules of thumb.

I recommended that our hospital build a weekly four-week forecast of pediatric respiratory admissions from five years of data, updated with state surveillance reports, and tie it to three triggers. At a forecast of 80% occupancy, the unit cross-trains two adult nurses and confirms pediatric supplies. At 90%, it opens four flex beds in the adjacent unit and postpones elective pediatric surgery. At 100%, it activates a transfer agreement with a second children's hospital and calls in the on-call pediatric hospitalist. A named manager reviews the forecast every Monday, compares last week's prediction with what actually happened and reports both to the nursing director.

Jesus told the crowds that when they saw a cloud rising in the west they said a shower was coming, and it did, yet they did not know how to interpret the present time (English Standard Version Bible, 2001/2016, Luke 12:54-56). A hospital that reads its own winter data as carefully as a farmer reads the sky will not meet the next surge at 2 a.m. without a plan.

Reply to Sofia

Sofia, your thread proposed forecasting next winter's admissions on your medical unit by averaging the last three winters, and you argued that a simple method would be easier for charge nurses to trust. I agree that simplicity matters, and Langabeer and Helton (2016) caution against models so complex that managers stop using them. The difficulty is that an average of past winters smooths away exactly the variation that causes trouble. A season that peaks early or high, like 2022 for children, would sit well above a three-year average, and the plan built on the average would be too small on the worst weeks.

Research suggests two improvements that keep the method simple. Shah et al. (2021) found that automated exponential smoothing, which gives more weight to recent weeks than older ones, outperformed fixed historical methods in winter, cutting median four-week error roughly in half compared with the older approach. Many spreadsheet and statistical tools can run such a model without specialist skills, so the method is not much harder to use than an average. Poon et al. (2019) studied six surge periods in Hong Kong's public hospitals and built a time series model of medical ward occupancy. In 10 of 15 hospitals, occupancy was significantly associated with influenza activity, while weather had little effect, and holidays such as Christmas and Chinese New Year lowered occupancy. Occupancy also varied by day of the week and by region.

Those findings suggest adding a leading indicator to your forecast. Weekly influenza activity reported by your state health department rises before admissions do, so a forecast that updates when that indicator climbs will warn you earlier than any average of past winters. You might also plan for the holiday dip and the rebound that follows it, which often brings a cluster of admissions just after New Year. Solomon told the sluggard to consider the ant, which gathers its food in summer without a commander to drive it (English Standard Version Bible, 2001/2016, Proverbs 6:6-8). A forecast is a way of gathering in summer. One more caution applies to any forecast your unit adopts: track its error every week and publish it alongside the forecast. If the forecast misses badly for several weeks, staff will learn to ignore it, so the manager who owns it should explain the misses and adjust the model rather than letting it lose credibility quietly. Measuring accuracy also shows whether the added indicator is earning its place. Over a season or two, that record becomes the evidence leaders need to staff the winter peak with confidence rather than by instinct. Would your charge nurses use a weekly forecast if it arrived as a single number with a range, posted on the unit board each Monday?

Reply to Garrett

Garrett, your thread argued that your hospital should close its eight-bed pediatric unit, which averages three patients a day, and transfer children to the children's hospital an hour away, freeing beds and nurses for adult care. Your operational logic is sound, since Langabeer and Helton (2016) note that low and highly variable volume makes a unit expensive to staff and hard to keep competent. Many hospitals have reached the same conclusion. Cushing et al. (2021) examined 4,720 U.S. hospitals from 2008 to 2018 and found that about one in five pediatric inpatient units disappeared over the decade and roughly one in eight pediatric beds went with them, with rural areas losing the most, so that close to a quarter of American children ended up living farther from inpatient pediatric care. Low-volume units without an associated intensive care unit were most likely to close.

The risk is what happens when every small unit makes the same decision. In a surge, the children's hospital fills, and the transfers you plan to rely on stop, as many community hospitals learned in 2022. Closing the inpatient unit also tends to erode the emergency department's readiness to care for children. Newgard et al. (2024) studied 467,932 injured children at 417 trauma centers and found that emergency departments with high pediatric readiness, whether sustained or newly gained, were associated with an estimated 643 additional lives saved, while low-readiness departments were associated with 729 additional preventable deaths, though the estimates were not statistically significant. The median readiness score at trauma centers fell from 81 to 77 between 2013 and 2021, largely because of declines in care coordination and quality improvement.

I would suggest a middle path: convert the unit to a short-stay pediatric observation area for stable children, keep a pediatric nurse champion on every shift in the emergency department and sign a formal surge agreement with the children's hospital that defines when each will hold or transfer. Jesus rebuked disciples who tried to send children away and said to let them come to him (English Standard Version Bible, 2001/2016, Mark 10:14). The financial picture also deserves a closer look before a decision. An eight-bed unit with a daily census of three loses money on most days, but the comparison should include what the hospital would spend on transfers, ambulance time and the emergency department hours children spend waiting for a bed elsewhere, as well as the adult beds the closure would actually free. If the children's hospital refuses transfers during the next surge, those children will stay in your building regardless, in a department no longer prepared for them. A decision made on average days can fail on the worst ones. Would your hospital keep its emergency department's pediatric readiness assessment current if the inpatient unit closed?

References

Cleary, B., Hudkins, M., Graven, P., Merkel, M., & Eriksson, C. (2026). Multi-hospital pediatric surge response to an RSV epidemic in a US state: Oregon, 2022. Disaster Medicine and Public Health Preparedness, 20, e38. https://doi.org/10.1017/dmp.2025.10303

Cushing, A. M., Bucholz, E. M., Chien, A. T., Rauch, D. A., & Michelson, K. A. (2021). Availability of pediatric inpatient services in the United States. Pediatrics, 148(1), e2020041723. https://doi.org/10.1542/peds.2020-041723

English Standard Version Bible. (2016). Crossway. (Original work published 2001)

Langabeer, J. R., II, & Helton, J. (2016). Health care operations management: A systems perspective (2nd ed.). Jones & Bartlett Learning.

Newgard, C. D., Rakshe, S., Salvi, A., Lin, A., Cook, J. N. B., Gausche-Hill, M., Kuppermann, N., Goldhaber-Fiebert, J. D., Burd, R. S., Malveau, S., Jenkins, P. C., Stephens, C. Q., Glass, N. E., Hewes, H., Mann, N. C., Ames, S. G., Fallat, M., Jensen, A. R., Ford, R. L., ... Remick, K. E. (2024). Changes in emergency department pediatric readiness and mortality. JAMA Network Open, 7(7), e2422107. https://doi.org/10.1001/jamanetworkopen.2024.22107

Poon, C. M., Wong, E. L. Y., Chau, P. Y. K., Yau, S. Y., & Yeoh, E. K. (2019). Management decision of hospital surge: Assessing seasonal upsurge in inpatient medical bed occupancy rate among public acute hospitals in Hong Kong. QJM: An International Journal of Medicine, 112(1), 11-16. https://doi.org/10.1093/qjmed/hcy217

Shah, K., Sharma, A., Moulton, C., Swift, S., Mann, C., & Jones, S. (2021). Forecasting the requirement for nonelective hospital beds in the National Health Service of the United Kingdom: Model development study. JMIR Medical Informatics, 9(9), e21990. https://doi.org/10.2196/21990