TABLE 2.
State-Fixed Multivariate Regressions of Hospital Application of ML and Other Predictive Modeling and Adoption of AI/ML in Workforce Development
| Characteristic | Margins 100% | 95% CI | P | |
|---|---|---|---|---|
| Indicator of whether the hospital uses ML or other predictive models (n = 2286) | ||||
| Non-for-profit hospital | Reference | |||
| For-profit hospital | −0.22 | −0.30 | −0.13 | <0.001 |
| Government-owned hospital | −0.25 | −0.30 | −0.20 | <0.001 |
| Bed size small | Reference | |||
| Bed size medium | 0.01 | −0.04 | 0.06 | 0.62 |
| Bed size large | 0.11 | 0.06 | 0.17 | <0.001 |
| Teaching hospital | −0.16 | −0.08 | 0.05 | 0.65 |
| ACO affiliation | 0.15 | 0.10 | 0.19 | <0.001 |
| Urban | Reference | |||
| Rural | −0.09 | −0.15 | −0.03 | 0.004 |
| Primary care HPSA | −0.01 | −0.07 | 0.05 | 0.72 |
| % population Black | 0.29 | 0.07 | 0.51 | 0.01 |
| ADI quantile 1 | Reference | |||
| ADI quantile 2 | −0.04 | −0.10 | 0.01 | 0.12 |
| ADI quantile 3 | −0.09 | −0.15 | −0.02 | 0.01 |
| ADI quantile 4 (the most vulnerable) | −0.10 | −0.18 | −0.03 | 0.01 |
| No. ML and other predictive modules adopted ranged from 1 to 8 (n = 1671) | ||||
| Non-for-profit hospital | Reference | |||
| For-profit hospital | −0.54 | −1.01 | −0.06 | 0.03 |
| Government-owned hospital | −0.18 | −0.48 | 0.11 | 0.22 |
| Bed size small | Reference | |||
| Bed size medium | −0.12 | −0.38 | 0.13 | 0.35 |
| Bed size large | −0.09 | −0.37 | 0.18 | 0.51 |
| Teaching hospital | 0.50 | 0.18 | 0.81 | 0.002 |
| ACO affiliation | 0.25 | 0.03 | 0.48 | 0.03 |
| Urban | Reference | |||
| Rural | −0.27 | −0.61 | 0.06 | 0.11 |
| Primary care HPSA | 0.01 | −0.28 | 0.29 | 0.97 |
| % population Black | −0.10 | −1.18 | 0.98 | 0.86 |
| ADI quantile 1 | Reference | |||
| ADI quantile 2 | −0.02 | −0.29 | 0.24 | 0.86 |
| ADI quantile 3 | −0.19 | −0.50 | 0.12 | 0.23 |
| ADI quantile 4 (the most vulnerable) | 0.02 | −0.35 | 0.39 | 0.92 |
| No. domains in which EHR was used ranged from 1 to 6 (n = 2391) | ||||
| Non-for-profit hospital | Reference | |||
| For-profit hospital | −1.03 | −1.31 | −0.74 | <0.001 |
| Government-owned hospital | −0.62 | −0.81 | −0.44 | <0.001 |
| Bed size small | Reference | |||
| Bed size medium | 0.11 | −0.06 | 0.29 | 0.21 |
| Bed size large | 0.26 | 0.06 | 0.46 | 0.01 |
| Teaching hospital | 0.26 | 0.01 | 0.51 | 0.05 |
| ACO affiliation | 0.36 | 0.21 | 0.51 | <0.001 |
| Urban | Reference | |||
| Rural | −0.34 | −0.54 | −0.14 | 0.001 |
| Primary care HPSA | −0.05 | −0.25 | 0.15 | 0.64 |
| % population Black | 0.13 | −0.63 | 0.89 | 0.73 |
| ADI quantile 1 | Reference | |||
| ADI quantile 2 | 0.01 | −0.19 | 0.22 | 0.90 |
| ADI quantile 3 | −0.40 | −0.63 | −0.17 | 0.001 |
| ADI quantile 4 (the most vulnerable) | −0.26 | −0.51 | 0.001 | 0.05 |
| No. areas in which AI/ML was used in workforce applications ranged from 1 to 5 (n = 1704) | ||||
| Non-for-profit hospital | Reference | |||
| For-profit hospital | −1.01 | −1.37 | −0.65 | <0.001 |
| Government-owned hospital | −0.82 | −1.04 | −0.60 | <0.001 |
| Bed size small | Reference | |||
| Bed size medium | −0.06 | −0.27 | 0.15 | 0.57 |
| Bed size large | 0.02 | −0.22 | 0.27 | 0.84 |
| Teaching hospital | 0.97 | 0.67 | 1.27 | <0.001 |
| ACO affiliation | 0.65 | 0.47 | 0.84 | <0.001 |
| Urban | Reference | |||
| Rural | −0.11 | −0.36 | 0.14 | 0.40 |
| Primary care HPSA | −0.09 | −0.34 | 0.16 | 0.49 |
| % population Black | 0.51 | −0.42 | 1.44 | 0.28 |
| ADI quantile 1 | Reference | |||
| ADI quantile 2 | −0.21 | −0.46 | 0.03 | 0.09 |
| ADI quantile 3 | −0.16 | −0.44 | 0.12 | 0.25 |
| ADI quantile 4 (the most vulnerable) | −0.40 | −0.70 | −0.09 | 0.01 |
Data source: 2022 AHA Annual Survey and the 2023 AHA IT Supplement.
The state fixed-effect logistic model was applied to “Hospital uses ML or other predictive models.” State fixed-effect linear regressions were applied to other outcome measures. Marginal effects were reported for all models.
Specific AHA survey questions:
Indicator of whether the hospital uses ML or other predictive models: Does your hospital use any ML or other predictive models that display output or recommendations (e.g., risk scores or clinical support) in your EHR or an App embedded in or launched by your EHR? 1=yes, 0=no.
The number of ML and other predictive modules adopted: Which of the following uses has your hospital applied ML or other predictive models? Please check all that apply.
a. Predicting health trajectories or risks for inpatients.
b. Identify high-risk outpatients to inform follow-up care.
c. Monitor health.
d. Recommend treatments.
e. Simplify or automate billing procedures.
f. Facilitate scheduling.
g. Other (operational process optimization).
h. Other (clinical use cases).
The number of domains in which EHR was used: Please indicate whether you have used electronic clinical data from the EHR or other electronic system in your hospital to:
a. Create an approach for clinicians to query the data.
b. Assess adherence to clinical practice guidelines.
c. Identify care gaps for specific patient populations.
d. Support a continuous quality improvement process.
e. Monitor patient safety (e.g., adverse drug events).
f. Identify high-risk patients for follow-up care using algorithms or other tools.
The number of areas in which AI/ML was used in workforce applications: Does your hospital use AI or ML in the following?
a. Predicting staffing needs.
b. Predicting patient demand.
c. Staff scheduling.
d. Automating routine tasks.
e. Optimizing administrative and clinical workflows.
f. None of the above.
ACO indicates accountable care organization; ADI, Area Deprivation Index; AHA, American Hospital Association; AI, artificial intelligence; EHR, electronic health record; HPSA, health professional shortage area; IT, information technology; ML, machine learning.