MMHA 5500 Week 10 Staff Retention Case Paper Example

Retention Case: Why Medical Assistants Leave One Urgent Care Clinic and Stay at the Other

Student Name

Walden University

MMHA 5500: Human Resource Management and Organizational Development and Leadership for Healthcare Administrators

Instructor Name

Month Day, Year

Retention Case: Why Medical Assistants Leave One Urgent Care Clinic and Stay at the Other

Introduction

Medical assistants keep leaving the South urgent care clinic in the composite Lakeside Outpatient Network, while the North clinic, paying identical rates under the same agreement, keeps its staff. That contrast is the spine of this case. Because pay, benefits and the job description are the same at both clinics, whatever explains the difference must lie elsewhere. The case tests three explanations, pay, schedule and supervision, against separation records, posted schedules and span of control, and then uses job embeddedness theory to frame what the stable clinic has that the other lacks. All figures are illustrative.

The Two Clinics Compared

Table 1 compares the two clinics over the past year.

Table 1

Medical Assistant Separations and Conditions by Clinic, Past Year (Illustrative)

Explanation One: Pay

Pay is ruled out as the primary cause of the difference, because the rates are identical, and none of the seven South leavers' exit interview notes names pay as the main reason, although three mention it alongside other reasons. A local comparison does show both clinics paying medical assistants somewhat less than nearby employers, including a hospital system's clinics. That gap may explain why the network loses some staff overall, and it deserves attention, but it cannot explain why South loses seven and North two. Turnover research has long found that pay is one influence among several, and that the work environment and relationships often explain differences that pay cannot (Hom et al., 2017).

Explanation Two: Schedule

The schedule explanation is tested with the network's schedule posting records. Both clinics post schedules four weeks ahead, as the agreement requires. But South changed medical assistants' shifts after publication far more often: 41 changes in the past year, against 6 at North. Most South changes moved a medical assistant to a different day or extended a shift at short notice to cover a gap. Exit interview notes from four of the seven South leavers mention unpredictable schedules, and two mention child care problems caused by late changes. The schedule explanation is supported, and it matters most for staff with obligations outside work, who are least able to absorb a change made days before a shift.

Explanation Three: Supervision

Supervision is tested in two ways. First, span of control: North's lead medical assistant supervises ten medical assistants and has held the role for six years. South's lead position has turned over three times in two years, and for four months it was vacant, with the clinic manager supervising medical assistants directly alongside nurses, providers and front desk staff, a span of more than twenty. Second, the schedule changes at South cluster in the months when the lead role was vacant or newly filled, suggesting that supervisory instability and schedule instability are linked. The supervision explanation is supported, and it may explain much of the schedule explanation: a lead who knows the staff and has time to plan can build a schedule that holds, while a vacant or new lead fills gaps by moving whoever is available.

What the Stable Clinic Has

Job embeddedness theory offers a way to see what North has that South lacks. Mitchell and colleagues argued that satisfaction alone does not explain staying; people also stay when they are embedded: they have links to colleagues and the community, they fit the job and its place in their lives and leaving would mean sacrificing things they value; and they found that embeddedness predicted staying beyond what job satisfaction and alternatives explained (Mitchell et al., 2001).

At North, medical assistants have long-standing links to a stable lead and to one another, a schedule that fits their lives because it rarely changes and a sense that leaving would mean giving up predictable hours and a team they know. At South, each of these is weaker. Links are thin because colleagues keep leaving and the lead keeps changing. Fit is poor because the schedule changes without warning. And there is little to sacrifice by leaving, since another employer offers similar pay with perhaps a more stable schedule. Embeddedness explains why the same pay and job description produce different results: what holds people at North is not the job itself but the web around it.

Recommendations

The network should, first, stabilize South's lead medical assistant role: fill it with an internal candidate if possible, give the role protected time for scheduling and supervision and pair the new lead with North's experienced lead for the first three months. Second, it should limit schedule changes after posting, setting a rule that changes require the medical assistant's agreement except in emergencies, and use the network's float pool to cover gaps rather than moving South's staff. Third, it should build links deliberately, for example by pairing each new South medical assistant with a peer for their first three months. Onboarding research identifies connection, the relationships and networks a new employee needs, as the level of onboarding organizations most often neglect (Bauer, 2010), and it is precisely the level South has been unable to provide while its lead role kept turning over. Finally, it should review the market pay gap for both clinics, recognizing that it affects retention across the network even though it does not explain the difference between the two clinics.

Measuring the Result

The network will track South's separations over the next year, compared with North's; the number of schedule changes after posting at South; and the tenure of South's lead. Exit interviews will ask specifically about schedule and supervision, so the network can see whether the explanations that survived testing continue to hold. If separations fall as schedule changes fall, the case's central finding will be confirmed; if they do not, the network will need to look again, perhaps at factors in South's local labor market that this case did not test.

References

Bauer, T. N. (2010). Onboarding new employees: Maximizing success. SHRM Foundation.

Hom, P. W., Lee, T. W., Shaw, J. D., & Hausknecht, J. P. (2017). One hundred years of employee turnover theory and research. Journal of Applied Psychology, 102(3), 530-545. https://doi.org/10.1037/apl0000103

Mitchell, T. R., Holtom, B. C., Lee, T. W., Sablynski, C. J., & Erez, M. (2001). Why people stay: Using job embeddedness to predict voluntary turnover. Academy of Management Journal, 44(6), 1102-1121. https://doi.org/10.2307/3069391