TABLE 3.
Decomposition Results Comparing Hospital AI/ML Adoption by ADI Q4 Versus ADI Q1–Q3
| No. ML and other predictive modules adopted | No. domains in which EHR was used | No. areas in which AI/ML was used in workforce applications | ||||
|---|---|---|---|---|---|---|
| Characteristic | Coef | P | Coef | P | Coef | P |
| ADI Q1–Q3 | 0.80 | <0.001 | 5.18 | <0.001 | 1.56 | <0.001 |
| ADI Q4 (the most vulnerable areas) | 0.62 | <0.001 | 4.59 | <0.001 | 0.89 | <0.001 |
| Difference | 0.18 | <0.001 | 0.59 | <0.001 | 0.67 | <0.001 |
| % | P | % | P | % | P | |
| Explained by the model | 78.94 | <0.001 | 95.73 | <0.001 | 65.94 | <0.001 |
| Explained by the individual factor | ||||||
| Government-owned hospital | 22.74 | <0.001 | 14.28 | <0.001 | 28.35 | <0.001 |
| Bed size large | 23.68 | <0.001 | 15.42 | 0.002 | — | — |
| Teaching hospital | — | — | — | — | 21.39 | <0.001 |
| ACO affiliated | 16.59 | <0.001 | 12.41 | <0.001 | 24.78 | <0.001 |
| Rural | 22.72 | <0.001 | 22.64 | 0.003 | — | — |
Data source: 2022 AHA Annual Survey and the 2023 AHA IT Supplement.
Decompositions were applied to 3 outcomes where ADI differences were significant: An indicator of whether a hospital uses ML or other predictive models, the number of domains in which EHR was used, and the number of areas in which AI/ML was used in workforce applications. Factors that significantly contributed to the observed differences >5% were presented.
Specific AHA survey questions:
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; IT, information technology; ML, machine learning.