DDHA 8006 Week 9 Evidence to Practice Application Example

From Appraised Evidence to One Decision: Funding Referral Coordinators at Tallgrass Medical Group, With Conditions and a Way to Judge It Later

Student Name

Walden University

DDHA 8006: Doctor of Healthcare Administration Foundational Concepts

Instructor Name

Month Day, Year

From Appraised Evidence to One Decision: Funding Referral Coordinators at Tallgrass Medical Group, With Conditions and a Way to Judge It Later

The Decision

The leadership council of Tallgrass Medical Group, the composite nine-practice primary care group studied in this course, must decide whether to fund three referral coordinator positions and a tracking queue in its record system, at an illustrative annual cost of about $210,000. This paper applies the evidence appraised earlier in the course to that decision. It recommends funding, under four conditions, and states in advance how the council will judge, after one year, whether the decision was right.

What the Evidence Supports, and How Far

The appraisal established three things. Referral failures occur at several stages, including scheduling, attendance and the return of information, and many of these stages are outside any single clinician's control (Mehrotra et al., 2011). Even with electronic ordering in an integrated system, many referrals go uncompleted (Patel et al., 2018). And the evidence for electronic referral and e-consult solutions comes mainly from integrated settings and measures access or perceptions rather than completion (Kim-Hwang et al., 2010; Vimalananda et al., 2015).

That evidence supports a decision to assign ownership of the referral loop and to measure completion. It does not prove that referral coordinators will raise completion at Tallgrass, because no study reviewed tested that intervention in a fragmented setting. The decision is therefore an application of evidence about the problem, combined with reasoning about the solution, under uncertainty. Stating that openly shapes the conditions below.

The Recommended Decision

Fund three referral coordinators, each covering three practices, and build a tracking queue that lists every open referral by days since order. Coordinators confirm that referrals reached the specialist, contact patients who have not scheduled, confirm attendance with specialist offices and request reports that have not arrived. Abnormal findings and overdue urgent referrals are escalated to the referring clinician the same day.

Table 1

Resource Implications (Illustrative Annual)

Most of the cost is staff, which means it can be reversed if the evaluation shows no benefit, and the coordinators could be redeployed to other front-office roles.

Conditions

The recommendation carries four conditions, each tied to a gap in the evidence.

First, measure before and after. Three hundred referrals chosen at random from the past twelve months will establish the baseline completion and closure rates before coordinators start, using the stage-by-stage definition the literature supports. Without a baseline, the council cannot judge the decision.

Second, stratify by setting. The sample will separate referrals to specialists on Tallgrass's record from referrals to outside practices, because the evidence comes mostly from integrated settings and the effect in fragmented settings is unknown.

Third, keep clinical responsibility visible. Escalation rules for abnormal findings and urgent referrals will be written before launch, so that administrative ownership does not dilute clinical attention, the main risk identified in the position paper's opposing case.

Fourth, protect patients and staff. The ethical safeguards developed for the inquiry, scripted voluntary calls by a nurse, coded data and process-level use of findings, will apply to coordinators' work as well.

How the Decision Will Be Judged

The council will judge the decision after twelve months against three measures, set now.

A decision judged only by its outcome invites hindsight; one judged against thresholds set in advance can be evaluated fairly even if circumstances change. If closure rises only for referrals inside Tallgrass's record, the council will learn that coordinators help where systems connect but not across boundaries, which would point toward agreements or e-consults rather than more staff.

Table 2

Evaluation Measures and Thresholds

The thresholds are set before the decision, so the evaluation cannot be shaped to fit the result.

Risks to the Decision

Three risks could undermine the decision. Coordinators might be absorbed into other front-office work during busy periods, so the queue would grow unworked; their job descriptions will protect at least 80 percent of their time for referrals, and the queue length will be reported weekly. Specialist practices might not cooperate with confirmation calls or report requests; the agreements with the five highest-volume practices are meant to address this, and lower-volume practices will be approached after the first quarter. And clinicians might stop checking for reports they expect, assuming coordinators will catch everything; the escalation rules and a brief explanation at each practice's staff meeting are designed to keep clinicians engaged where their judgment matters.

Alternatives Considered

Two alternatives were considered. Buying a commercial referral management platform was deferred because the evidence for such platforms comes from integrated settings and because the problem's stages have not yet been measured at Tallgrass. Relying on physician alerts alone was rejected because it leaves the chasing work with clinicians, which the problem statement identified as the reason loops stay open. Both may be revisited after the first year's evidence. If coordinators raise closure for referrals inside Tallgrass's record but not for outside practices, a platform that connects across organizations would become a stronger candidate.

Conclusion

The appraised evidence supports making the referral loop an organizational responsibility at Tallgrass but cannot prove that referral coordinators will work in a fragmented market. The recommended decision funds coordinators under four conditions that answer the evidence's gaps, at a cost that can be reversed, and sets in advance the measures by which the council will judge it. That is what applying evidence to practice looks like when the evidence is incomplete.

References

Kim-Hwang, J. E., Chen, A. H., Bell, D. S., Guzman, D., Yee, H. F., & Kushel, M. B. (2010). Evaluating electronic referrals for specialty care at a public hospital. Journal of General Internal Medicine, 25(10), 1123-1128. https://doi.org/10.1007/s11606-010-1402-1

Mehrotra, A., Forrest, C. B., & Lin, C. Y. (2011). Dropping the baton: Specialty referrals in the United States. The Milbank Quarterly, 89(1), 39-68. https://doi.org/10.1111/j.1468-0009.2011.00619.x

Patel, M. P., Schettini, P., O'Leary, C. P., Bosworth, H. B., Anderson, J. B., & Shah, K. P. (2018). Closing the referral loop: An analysis of primary care referrals to specialists in a large health system. Journal of General Internal Medicine, 33(5), 715-721. https://doi.org/10.1007/s11606-018-4392-z

Vimalananda, V. G., Gupte, G., Seraj, S. M., Orlander, J., Berlowitz, D., Fincke, B. G., & Simon, S. R. (2015). Electronic consultations (e-consults) to improve access to specialty care: A systematic review and narrative synthesis. Journal of Telemedicine and Telecare, 21(6), 323-330. https://doi.org/10.1177/1357633X15582108