BUSI 494 Week 8 Discussion Board Forum 4 Example

Discussion Board Forum 4: Technology Strategy and the Future of Health Care Organizations

BUSI 494 Healthcare Administration Capstone, Discussion Board Forum 4

Thread by [Student Name]

The group, the hospital and the replies to other groups are composites; the textbook, Scripture and the peer-reviewed sources cited are real.

Our group's case is an independent 180-bed community hospital that has received offers in the past six months for an ambient documentation tool, a sepsis prediction model, an automated prior authorization service and a chatbot for scheduling. Each vendor promises savings, and several physicians are eager to try the documentation tool after hearing about it from colleagues at other hospitals. The board has asked whether the hospital has an artificial intelligence strategy. The honest answer is that it has a stack of proposals. No one has compared the offers, estimated their total cost or asked how each would affect patients, staff and the hospital's legal exposure. The information technology department, already stretched by an electronic record upgrade, has warned that it cannot support four new tools at once.

Moseley (2018) treats technology choices as strategic decisions that should flow from the organization's mission, environment and capabilities, not from vendor timing. Applied here, the first question is not which tool to buy but how the hospital will decide which tools to adopt, how it will check that they work for its patients and who will be accountable when they do not. A strategy also means choosing what not to do, so that scarce staff time goes to the few tools most likely to help.

The evidence shows why governance matters, especially for a hospital without a large data science team. Nong et al. (2025) analyzed the 2023 American Hospital Association survey and found that 65% of U.S. hospitals used predictive models, and 79% of those relied on models supplied by their electronic health record developer. Only 61% of those hospitals had evaluated their models for accuracy using their own data, and only 44% had evaluated them for bias. Hospitals that built their own models, had higher margins or belonged to systems were more likely to evaluate them, which suggests that independent hospitals like ours are at particular risk of using tools no one has checked locally.

The evidence on popular tools is also mixed. Preiksaitis et al. (2026) studied an ambient AI scribe in an academic emergency department and found that encounters using it required about 28% less on-shift documentation time, yet only 38% of attending physicians used the tool, and a few heavy users accounted for most of the use. Stults et al. (2026) found that in inpatient settings at a large California organization, ambient AI generated only about 3% of notes and did not change physicians' daily time in notes. Benefits appear real in some settings but depend heavily on workflow and adoption, which is why a pilot should measure both the tool's effect and how many clinicians actually use it.

Our group recommends three steps. First, form an AI governance committee with clinical, information technology, compliance and patient representatives, charged with approving any tool and requiring local validation for accuracy and bias. Second, pilot no more than two tools in the first year, chosen for clear problems: the documentation tool in the emergency department, where evidence is strongest, and automated prior authorization, which addresses discharge delays. Third, define success measures in advance, such as documentation time, physician burnout scores and authorization turnaround, and stop any pilot that does not meet them. The committee would report results to the board twice a year, adding AI measures to the balanced scorecard the hospital already uses for strategic oversight. A hospital that buys AI without governance is not adopting technology; it is adopting someone else's untested assumptions.

Daniel records that God gave the young men learning and skill and Daniel understanding (English Standard Version Bible, 2001/2016, Daniel 1:17). Knowledge and new tools are gifts, but understanding, the wisdom to use them well, is what leaders must supply.

Reply to Group 3

Group 3, your thread presented ambient AI scribes as a solution to physician burnout. We share your hope, but the early evidence suggests a more cautious claim, at least until more hospitals report results. Preiksaitis et al. (2026) found meaningful time savings in an emergency department, about 28% less documentation time during shifts when the tool was used, but fewer than four in ten attending physicians adopted it, and use clustered among a small group. Stults et al. (2026) found very low inpatient use and no change in the time physicians spent on notes each day, with physicians asking for features the tool lacked. Moseley (2018) emphasizes that implementation, including workflow redesign and training, determines whether a strategy produces results, and new tools are no exception. Burnout also has causes beyond documentation, such as staffing and workload, so a scribe alone is unlikely to solve it. Exodus describes Bezalel as filled with skill and knowledge for every craft (English Standard Version Bible, 2001/2016, Exodus 31:3), and good tools still depend on skilled people working within well-designed systems. Would your group pair a scribe pilot with other burnout measures, such as protected administrative time, and track burnout scores before and after? That design would show whether the tool itself made a difference or simply arrived alongside other changes.

Reply to Group 5

Group 5, your thread raised concern that AI tools could treat some patients unfairly, and we think that concern belongs at the center of any hospital's strategy. Obermeyer et al. (2019) studied a widely used commercial algorithm that predicted which patients needed extra care management and showed that the tool predicted future spending rather than illness, and since historical spending on equally sick Black patients had been lower, it ranked them as healthier than they were; correcting the problem would have substantially increased the share of Black patients flagged for additional help. Nong et al. (2025) found that fewer than half of hospitals using predictive models checked them for bias with their own data. Moseley (2018) notes that strategy must fit an organization's values as well as its market, and fairness is one of those values. Leviticus commands that judgment be done in righteousness, without partiality to the poor or deference to the great (English Standard Version Bible, 2001/2016, Leviticus 19:15). Would your group require that every predictive model be tested for differences in performance by race, sex and insurance before going live? A small hospital could ask vendors to provide that analysis as a condition of purchase, which shifts some of the testing burden to the companies best equipped to do it.

References

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

Moseley, G. B., III. (2018). Managing health care business strategy (2nd ed.). Jones & Bartlett Learning.

Nong, P., Adler-Milstein, J., Apathy, N. C., Holmgren, A. J., & Everson, J. (2025). Current use and evaluation of artificial intelligence and predictive models in US hospitals. Health Affairs, 44(1), 90-98. https://doi.org/10.1377/hlthaff.2024.00842

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342

Preiksaitis, C., Alvarez, A., Winkel, M., Karamatsu, M., Brown, I., Sama, N., Morris, L., Lee, J., Gubbels, A., Wahl, E., Frye, A., & Rose, C. (2026). Ambient artificial intelligence scribe adoption and documentation time in the emergency department. Annals of Emergency Medicine, 87(5), 569-574. https://doi.org/10.1016/j.annemergmed.2025.12.017

Stults, C. D., Deng, S., Martinez, M. C., Wilcox, J., Szwerinski, N., Wilde, J., Liu, R. C., Rabbani, N., Driscoll, S., & Jones, V. G. (2026). Ambient artificial intelligence scribe implementation in inpatient setting. Journal of Hospital Medicine. Advance online publication. https://doi.org/10.1002/jhm.70451