HLTH5643 Module 4 implementation plan example

Nearly 800 Training Hours and Fourteen Days at the Elbow: An Implementation Plan for a Hospital-Wide Bed Management System

Student Name

American College of Education

HLTH5643: Information Systems Management for Healthcare Administrators

Module 4 Assignment

Instructor Name

September 25, 2028

Go-Live Strategy and Timeline

The composite 318-bed hospital has chosen the standalone bed management product, Option B from the previous module, subject to the full cost comparison later in the course. This plan assumes that comparison confirms the choice and the contract is signed on October 1. The first decision is whether to go live all at once or unit by unit. A phased rollout reduces risk in most system implementations, but a bed board that covers only half the house is of little use to a house supervisor who must place patients anywhere. The plan therefore takes a mixed approach: the core bed status and assignment functions go live across all inpatient units, the emergency department and environmental services on the same day, while secure messaging and the charge nurse admission pause are switched on 30 days later, once the core process is stable.

The timeline runs seven months. Months one to four cover configuration, building the two-way interface with the electronic record and loading the hospital's bed and unit structure. Month four adds integrated testing with real admission, discharge and transfer messages from a test environment, and a full downtime drill. Months five and six are for training, and go-live is set for the first Tuesday in May, avoiding the Monday surge in admissions and the winter respiratory season. The go-live date is chosen for the census it will meet, not for the calendar.

Training by Role

Training is designed by role, because each group uses a small part of the system in a very different way. Pantaleoni et al. (2015), describing a successful physician training program during a large record implementation at a children's hospital, found that learners' comments focused on the length and timing of sessions, the learning environment, the quality of instructors and how specific the training was to their role, and the authors recommended grouping learners by common workflow. Each session here is built from the scenarios used in the vendor demonstrations, which came from real traced admissions, so that staff practice the cases they will actually see.

The nine house supervisors, who will use the system more than anyone, receive eight hours each, including a half day practicing a full simulated shift: 72 hours. The 120 charge nurses receive two hours each, 240 hours. The 85 environmental services staff receive ninety minutes each on the mobile device, 127.5 hours, in small groups on their own shifts with an instructor from their department. The 110 emergency department nurses need only to see bed assignments and so receive a 30-minute online module, 55 hours. The 40 unit secretaries and 22 transport staff receive one hour each, 62 hours. Thirty super users, drawn from every group and every shift, receive eight additional hours, 240 hours. The total is 796.5 hours. At an average loaded wage of $46 an hour, the staff time costs about $36,600, which must be included in the implementation budget rather than absorbed by departments.

Proficiency Before Access

Attending a class is not the same as being able to use the system. Each group completes a short proficiency check at the end of training, performed on the training environment: a housekeeper marks a room clean and flags a room needing extra cleaning; a charge nurse views pending discharges and updates an expected discharge time; a supervisor assigns three beds from a queue and resolves a conflict. Staff who do not pass practice with a super user and repeat the check. System access is activated only after the check is passed, and unit managers receive a weekly list of staff not yet trained so that no one arrives for a shift on go-live day unable to use the system. Physicians are not required to train, since they do not use the system directly, but a one-page summary of how bed assignment will change is sent to the medical staff.

Support After Go-Live

Go-live support runs for fourteen days. A command center in the information systems department is staffed around the clock for the first seven days by an analyst, an interface specialist and a vendor representative, and on day and evening shifts for the second week. Every unit and the environmental services office has a super user on each shift for the first seven days, working alongside staff rather than doing the work for them, and on busy shifts during the second week. Issues are logged in a single tracking list, triaged by the command center into urgent, which blocks patient placement and must be addressed within one hour, and routine, and reviewed at two short daily huddles, at 9 a.m. and 9 p.m., attended by the supervisors, the environmental services manager and the project lead.

The old tools must be retired deliberately, or the new system will become one more place to check. The whiteboard in the supervisor's office will be kept, updated from the new system, for the first 72 hours as a fallback, then removed. The supervisor's private spreadsheet is the more important workaround to retire. The supervisor who built it has been appointed lead super user and helped design the training scenarios, and on day three will personally archive it at the evening huddle. A workaround ends when the person who built it decides it is no longer needed.

Expecting Resistance

Lorenzi and Riley (2000) observe that the major challenges to information system success are often more behavioral than technical, and that people with low psychological ownership of a system who resist its implementation can bring a technically excellent system to its knees, while effective leadership can sharply reduce that resistance. Three groups have reasons to resist. Environmental services staff are being asked to update a device at every bedside, which can feel like surveillance; their manager and two housekeepers helped write the training, and the plan states clearly that the data will be used to improve dispatch, not to time individual workers. Charge nurses will lose the informal ability to hold beds quietly at shift change; the admission pause feature, switched on after 30 days, gives them a visible alternative. Emergency physicians may see little benefit at first; their lead physician will receive the weekly boarding time data from go-live forward, so that improvement is visible to the group most affected by delay.

Early Measures of Success

The team will know within a month whether the system is being used as intended. The first measure is the share of cleaning completions entered at the bedside within five minutes of the housekeeper finishing, estimated from device timestamps and spot checks, aiming for nine in ten by the close of the fourth week. The second measure, reported weekly, tracks how long a patient waits between the decision to admit and a bed being assigned, using the median; it stood at 94 minutes in the traced sample. The third is a simple observation: whether any bed placement in a sample of 20 each week relies on information outside the system, which would signal that a new workaround has formed.

If the first measure falls below 75 percent in week two, the command center will visit environmental services on each shift to find the cause before assuming the problem is compliance. Kaplan and Harris-Salamone (2009) emphasize that health information technology projects succeed or fail through the fit between the system and the work, and a low bedside update rate is more likely to reveal a device, network or workflow problem than unwilling staff.

Conclusion

The plan takes the bed management system from contract to stable use in about eight months. It goes live across the whole house at once for the functions that only work hospital-wide, phases the rest, trains each group for its own work at a cost of about 800 hours of staff time, checks proficiency before granting access and supports staff at the elbow for two weeks. It also plans for the end of the old tools and the people most likely to resist, and sets early measures that will show within a month whether the hospital has changed how bed information moves or simply added another screen.

References

Kaplan, B., & Harris-Salamone, K. D. (2009). Health IT success and failure: Recommendations from literature and an AMIA workshop. Journal of the American Medical Informatics Association, 16(3), 291-299. https://doi.org/10.1197/jamia.M2997

Lorenzi, N. M., & Riley, R. T. (2000). Managing change: An overview. Journal of the American Medical Informatics Association, 7(2), 116-124. https://doi.org/10.1136/jamia.2000.0070116

Pantaleoni, J. L., Stevens, L. A., Mailes, E. S., Goad, B. A., & Longhurst, C. A. (2015). Successful physician training program for large scale EMR implementation. Applied Clinical Informatics, 6(1), 80-95. https://doi.org/10.4338/ACI-2014-09-CR-0076