Difference between revisions of "Healthcare AI Use Cases"
From PatientRecovery
| (24 intermediate revisions by one user not shown) | |||
| Line 1: | Line 1: | ||
==ClosedLoop AI Use Cases== | ==ClosedLoop AI Use Cases== | ||
| − | + | Providers & ACOs <br> | |
| − | * [https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619?utm_source=miragenews&utm_medium=miragenews&utm_campaign=news Predict patients most likely to miss appointments] | + | * [https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619?utm_source=miragenews&utm_medium=miragenews&utm_campaign=news '''Appointment No-Shows''' - Predict patients most likely to miss appointments] |
* [https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619?utm_source=miragenews&utm_medium=miragenews&utm_campaign=news Predict patients most likely to acquire infections like sepsis] | * [https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619?utm_source=miragenews&utm_medium=miragenews&utm_campaign=news Predict patients most likely to acquire infections like sepsis] | ||
* [https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619?utm_source=miragenews&utm_medium=miragenews&utm_campaign=news Predict patients most likely to benefit from periodic check ups] | * [https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619?utm_source=miragenews&utm_medium=miragenews&utm_campaign=news Predict patients most likely to benefit from periodic check ups] | ||
| − | Health | + | * Leakage - Network Integrity - Out of Network |
| − | * [https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619?utm_source=miragenews&utm_medium=miragenews&utm_campaign=news Predict patient readmissions | + | * Total Risk: Who will be my most expensive patients this year |
| − | * [https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619?utm_source=miragenews&utm_medium=miragenews&utm_campaign=news Predict | + | * ED Over-Utilization: Which of my patients would most benefit from establishing a relationship with a primary care provider? |
| + | * Readmissions: Which patients are most likely to be readmitted to the hospital? | ||
| + | * Transportation: Which patients are most likely to need help with transportation in order to make it to their appointments | ||
| + | * Rising Risk: Which patients are likely to see large increases in their overall health risk over the next 30/60 days? | ||
| + | * Chronic Disease: Which patients are most likely to develop or progress in the severity of a chronic disease | ||
| + | * Medication Adherence: Which patients are most likely to be noncompliant with medications? | ||
| + | |||
| + | Payers and Health Plans <br> | ||
| + | * Risk Adjustment - Suspect Diagnosis | ||
| + | * [https://closedloop.ai/preventable-hospitalizations/ Preventable Hospitalizations] | ||
| + | * [https://closedloop.ai/readmissions/ Readmissions] - [https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619?utm_source=miragenews&utm_medium=miragenews&utm_campaign=news Predict patient readmissions] | ||
| + | * [https://closedloop.ai/chronic-disease/ Chronic Disease Onset and Progression] - [https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619?utm_source=miragenews&utm_medium=miragenews&utm_campaign=news Predict onset or progression of chronic diseases.] | ||
| + | * [https://closedloop.ai/total-risk/ Patients with highest overall risk in the next 6 months or year] | ||
| + | * Trend: Which members are likely to see large increases in their overall health risk over the next three to six months? | ||
| + | * Readmissions: Which members are most likely to be readmitted to the hospital? | ||
| + | * Medication Adherence: Which members are most likely to be noncompliant with medication plans? | ||
| + | * Palliative Care: Which members are most likely to benefit from discussions with a palliative care specialist? | ||
| + | * ED Over-Utilization: Which members are most likely to benefit from establishing a relationship with a primary care provider? | ||
| + | * Disenrollment: Which members will most likely to disenroll? | ||
| + | * Fraud: How do new data sources allow health plans to spot fraud sooner? | ||
| + | * Payment Integrity: How can AI help ensure proper reimbursement? | ||
| + | |||
| + | Pharma & Life Science <br> | ||
| + | * Biomarkers: Which patients will have an increased success rate based on biological factors? | ||
| + | * Drug-Combinations: Which drug combinations are most likely to be successful? | ||
| + | * Segmentation: Which groups of patients respond differently to treatment? | ||
| + | * Strategy: Which subpopulations should be included/excluded based off of predicted success rates? | ||
| + | * Responsive: Which patients are responding to treatment? | ||
| + | * Events: Which patients are most likely to experience adverse reactions? | ||
| + | * Effectiveness: How will clinical trial results translate into real world effectiveness? | ||
| + | * Value: What improvement in outcomes will a new treatment generate over existing therapies? | ||
| + | * Switching: Which factors are most relevant in understanding which patients switch drugs? | ||
| + | * Marketing: Which physicians can we market to? | ||
| + | |||
| + | |||
| + | Other <br> | ||
| + | * [https://www.beckershospitalreview.com/artificial-intelligence/ge-university-hospitals-cleveland-partner-on-ai-lung-imaging-tech.html '''Identify patients' with collapsed lungs''' - artificial intelligence-enabled mobile X-ray system flags images of patients who need immediate intercession, ensuring that patients with collapsed lungs receive more timely care. (7/8/20)] | ||
| + | * [https://www.beckershospitalreview.com/artificial-intelligence/mayo-clinic-to-implement-ai-powered-predictive-patient-triage-platform.html '''Predicts patients' hospitalization risk and triages them to appropriate care''' - uses AI tech, EHR data and a questionnaire to perform clinical intake of patients visiting ER, Urgent Care or Home (6/30/20)] | ||
| + | * [https://jamanetwork.com/journals/jama/fullarticle/2665774 Diagnosed breast cancer at a higher rate than 11 pathologists. AI model using algorithms and deep learning (12/12/17)] | ||
| + | * Reducing cost of Rx at a system level without sacrificing quality | ||
| + | |||
| + | * [https://www.kensci.com/solutions/ops/ Emergency Department Demand Prediction] | ||
| + | * [https://www.kensci.com/solutions/ops/ Emergency Department Utilization Prediction] | ||
| + | * [https://www.kensci.com/solutions/ops/ Length of Stay Prediction] | ||
Latest revision as of 10:27, 10 July 2020
ClosedLoop AI Use Cases
Providers & ACOs
- Appointment No-Shows - Predict patients most likely to miss appointments
- Predict patients most likely to acquire infections like sepsis
- Predict patients most likely to benefit from periodic check ups
- Leakage - Network Integrity - Out of Network
- Total Risk: Who will be my most expensive patients this year
- ED Over-Utilization: Which of my patients would most benefit from establishing a relationship with a primary care provider?
- Readmissions: Which patients are most likely to be readmitted to the hospital?
- Transportation: Which patients are most likely to need help with transportation in order to make it to their appointments
- Rising Risk: Which patients are likely to see large increases in their overall health risk over the next 30/60 days?
- Chronic Disease: Which patients are most likely to develop or progress in the severity of a chronic disease
- Medication Adherence: Which patients are most likely to be noncompliant with medications?
Payers and Health Plans
- Risk Adjustment - Suspect Diagnosis
- Preventable Hospitalizations
- Readmissions - Predict patient readmissions
- Chronic Disease Onset and Progression - Predict onset or progression of chronic diseases.
- Patients with highest overall risk in the next 6 months or year
- Trend: Which members are likely to see large increases in their overall health risk over the next three to six months?
- Readmissions: Which members are most likely to be readmitted to the hospital?
- Medication Adherence: Which members are most likely to be noncompliant with medication plans?
- Palliative Care: Which members are most likely to benefit from discussions with a palliative care specialist?
- ED Over-Utilization: Which members are most likely to benefit from establishing a relationship with a primary care provider?
- Disenrollment: Which members will most likely to disenroll?
- Fraud: How do new data sources allow health plans to spot fraud sooner?
- Payment Integrity: How can AI help ensure proper reimbursement?
Pharma & Life Science
- Biomarkers: Which patients will have an increased success rate based on biological factors?
- Drug-Combinations: Which drug combinations are most likely to be successful?
- Segmentation: Which groups of patients respond differently to treatment?
- Strategy: Which subpopulations should be included/excluded based off of predicted success rates?
- Responsive: Which patients are responding to treatment?
- Events: Which patients are most likely to experience adverse reactions?
- Effectiveness: How will clinical trial results translate into real world effectiveness?
- Value: What improvement in outcomes will a new treatment generate over existing therapies?
- Switching: Which factors are most relevant in understanding which patients switch drugs?
- Marketing: Which physicians can we market to?
Other
- Identify patients' with collapsed lungs - artificial intelligence-enabled mobile X-ray system flags images of patients who need immediate intercession, ensuring that patients with collapsed lungs receive more timely care. (7/8/20)
- Predicts patients' hospitalization risk and triages them to appropriate care - uses AI tech, EHR data and a questionnaire to perform clinical intake of patients visiting ER, Urgent Care or Home (6/30/20)
- Diagnosed breast cancer at a higher rate than 11 pathologists. AI model using algorithms and deep learning (12/12/17)
- Reducing cost of Rx at a system level without sacrificing quality
