Difference between revisions of "Healthcare AI Use Cases"

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*  [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://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)]
 
* [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)]
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* Reducing cost of Rx at a system level without sacrificing quality
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* [https://www.kensci.com/solutions/ops/ Emergency Department Demand Prediction]
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* [https://www.kensci.com/solutions/ops/ Emergency Department Utilization Prediction]
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* [https://www.kensci.com/solutions/ops/ Length of Stay Prediction]

Revision as of 10:03, 10 July 2020

ClosedLoop AI Use Cases

Hospitals

Health Insurers


Providers & ACOs

  • 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

  • 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