Background
Methods
Study design
Sample
Study procedures
Phase 1: Domain identification, item generation, and survey formation
Phase 2: Content validation and prioritization
Data analysis
Results
Category 1: demographics
Characteristics (n = 50) | n (%) |
---|---|
Country | |
Finland | 30 (60.0) |
Italy | 9 (18.0) |
Spain | 11 (22.0) |
Age | |
< 29 years | 2 (4.0) |
30–39 years | 17 (34.0) |
40–49 years | 18 (36.0) |
50–59 years | 9 (18.0) |
> 60 years | 4 (8.0) |
Gender | |
Female | 37 (75.5) |
Male | 12 (24.5) |
Work tenure | |
< 1 year | 0 (0.0) |
1–5 years | 3 (6.0) |
6–10 years | 12 (24.0) |
11–20 years | 17 (34.0) |
21–30 years | 12 (24.0) |
> 30 years | 6 (12.0) |
Profession | |
Chief physician | 9 (18.0) |
Chief nurse | 4 (8.0) |
Physician | 16 (32.0) |
Nurse | 13 (26.0) |
Other | 8 (16.0) |
Response alternatives | n (%) | |
---|---|---|
Reporting: | I have manual reports available to see what the case/situation is (what happened) | 12 (24.0) |
Dashboards: | I have multiple information systems and a lot of data that I can individually use to analyze and measure the case/situation (why did it happen) | 31 (62.0) |
Predictions: | My work and decision-making are supported by smart hospital systems that merge data from multiple data sources and give predictions, recommendations and/or measurable results to anticipate the future (what will happen) | 2 (4.0) |
Prescriptive: | I have utilized smart hospital systems to augment and complement my human work and decision-making process with consistent and measurable results (how can we make it happen) | 1 (2.0) |
Innovations: | I am creatively utilizing, innovating and developing use of smart hospital systems and finding new ways to stay a step ahead in patient-centric care | 3 (6.0) |
None of the above
| 1 (2.0) |
Category 2: Relevancy of unit-level recommendations for operation
Category 3: Relevancy of unit-level recommendations for patient-predicted perioperative processes/patient flows
Category 4: Importance of patient-level functionalities in the UI
Statements | I – CVI | WRP (ranking withing the category) | |
---|---|---|---|
Relevancy of unit-level recommendations for operation. | 1. It is important that AI is able to make individual patient profiles based on previous data | 0.82 | 2 |
2. It is important that AI can suggest the best possible timing for a treatment or visit based on patient risks and predicted patient flow | 0.9 | 3 | |
3. It is important that AI recognizes if the patients are in risk for adverse events during the care | 0.96 | 1 | |
4. It is important that AI takes into account uncontrollable/external factors (weather, major events, holidays) while scheduling for the care | 0.42 | 9 | |
5. It is essential that AI identifies the most relevant examinations and tests when planning a treatment event | 0.86 | 5 | |
6. It is important that AI is able to recognize unnecessary laboratory tests for individual patients for the treatment/care | 0.66 | 13 | |
7. It is important for AI to identify patient level patterns that cause last minute cancellation of treatment/care | 0.72 | 11 | |
8. AI must estimate the duration of all phases of treatment events | 0.54 | 10 | |
9. AI idenfies and takes into account adverse or atypical events when predicting the care pathway and duration of its phases. | 0.71 | 12 | |
10. It is important that AI recommends the best time for the planned treatment for the patient, taking into account other scheduled patients, patient flow and available resources. | 0.84 | 7 | |
11. It is important for AI to identify patterns from the live data that predicts deterioration in the patient`s chronic diseases. | 0.82 | 6 | |
12. It is important that AI is able to use data from home sensors, wearables and robots when predicting individual patient’s symptom progression | 0.76 | 8 | |
13. It is important that AI recognizes patterns from patient data that are associated with deterioration during the following 24 h | 0.88 | 4 | |
Relevancy of unit-level recommendations for patients predicted perioperative process/patient flows | 1. It is important that AI is able to predict available resources for certain time points based on data of internal and external factors | 0.86 | 2 |
2. It is important that AI is able to estimate the needed time resources for the scheduled day. | 0.84 | 4 | |
3. It is important that AI is able to recognize the possible factors and patterns causing adverse events after care or prolonged need for care | 0.82 | 1 | |
4. It is important that AI is able to recognize the days of increased need for care and increased need for resources (“high flow days”) and the factors causing those | 0.88 | 3 | |
5. It is important that AI is able to predict the days of decreased personnel resources in care | 0.76 | 5 | |
6. It is important that AI is able to predict the average duration of provided care | 0.76 | 6 | |
Importance of patient-level functionalities in the UI | 1. The user interface has a visualization of predicted patient flow and a reasoning behind it for a particular patient (predicted duration of each phase of his/her care path) | 0.78 | 2 |
2. The user interface has functionalities for finding an appropriate and right timing for a particular patient’s treatment | 0.88 | 3 | |
3. The user interface updates the visualization of the patient flow for a particular patient during care based on existing data enriched with live data | 0.73 | 4 | |
4. The user interface has a report/list of the patients whose treatment is anticipated to be at risk for cancellation | 0.66 | 5 | |
5. The user interface updates the visualization of predicted evolution of patient’s condition based on the historical and live patient data | 0.84 | 1 | |
Importance of unit-level functionalities in the UI | 1. The user interface has a visualization of units/hospitals general patient flow (all predicted patients flows) | 0.72 | 3 |
2. The user interface updates the visualization of the patient flow for a particular patient during care based | 0.76 | 2 | |
3. The user interface has a view of the recommended order of patients’ treatment | 0.82 | 1 | |
4. The user interface has a report/list of similar patients to replace cancelled ones | 0.70 | 4 | |
5. The user interface has a view of predicted changes in the patient flow based on uncontrollable factors like weather, major events and holiday seasons | 0.54 | 5 | |
Importance of functionalities in the UI (unit resources) | 1. The user interface has a functionality to check if staff availability is anticipated to be limited during planned treatment time | 0.78 | 1 |
2. The user interface has a functionality to check if hospital capacity is anticipated to be limited during plan planned treatment time | 0.86 | 2 | |
3. The user interface has a visualization of predicted hospital capacity as a replicate of hospital environment (i.e. digital twin) | 0.56 | 3 | |
4. The user interface has a report/list of predicted financial impacts of the clinical care process | 0.44 | 4 |
Category 5: Importance of unit-level functionalities in the UI
Category 6: Importance of functionalities in the UI
Data summary
The highest ranked statements | I – CVI |
---|---|
It is important that AI recognizes if the patients are in risk for adverse events during the care | 0.96 |
It is important that AI is able to predict available resources for certain time points based on data of internal and external factors | 0.86 |
The user interface updates the visualization of predicted evolution of patient’s condition based on the historical and live patient data | 0.84 |
The user interface has a view of the recommended order of patients’ treatment | 0.82 |
The user interface has a functionality to check if hospital capacity is anticipated to be limited during plan planned treatment time | 0.86 |