의료 산업

의료 산업의 고부가가치 분석 과제

• 재입원 감소

  Patient readmission results in significant costs for payers, hospitals, and patients. Using DataRobot’s enterprise AI platform to predict and prevent hospital readmissions leads to more efficient use of scarce hospital resources while improving the overall quality of care that patients receive.

• ICU 및 ED 활용에 대한 정확한 예측

  High costs and periodic scarcity of critical care resources are two key reasons why ICU operations must be improved. Using an enterprise AI platform to create models that more accurately predict which patients need and do not need intensive care and to anticipate optimum staffing levels represents an enormous cost saving opportunity for hospitals.

• 처방된 약물 요법을 지키지 않을 가능성이 높은 환자 또는 회원 판단

  Patients or members with chronic diseases who do not consistently take their medications lead to more than $100 billion in preventable costs annually. Using DataRobot to create models that identify the patients or members who are less likely to adhere to prescribed drug regimens and predict their behavioral drivers helps create the right intervention plan to increase medication adherence.

• 사기성 보험금 지불 활동 식별

  Healthcare fraud can be costly, but it is also too expensive and inefficient to investigate every suspicious claim. Using DataRobot’s enterprise AI platform, organizations build accurate predictive models to identify and prioritize the most likely fraudulent activity, allowing for more effective deployment of resources to investigate, and optimization of customer satisfaction.

• 이탈 위험이 있는 회원 또는 환자 표시

  Payers and providers lose money when members decline renewal with their health plan or when patients do not return to their facilities. Payers and providers can use DataRobot to incorporate patient retention risks into their organization’s workflow, leading to reductions in non-renewals and patients not returning for further treatments.

• 의료기관에 원내 감염 위험 환자 경고

  환자는 입원 중에 혈류 감염에 더 취약해집니다. 이로 인해 재입원 등, 비용이 많이 드는 상황이 발생합니다.DataRobot은 어떤 환자가 패혈증이나 CLABSI 감염 위험이 높은지 예측하여 의사에게 추가 진단이나 테스트를 해볼 것을 자동으로 고지할 수 있습니다.

• 환자나 회원의 입원 기간 예측

  One of the primary predictors of cost is length of stay. Longer stays not only place patients at higher risk of hospital-acquired conditions, but they also constrain hospital bed availability and physician time. DataRobot can help providers and payers predict the length of an inpatient stay, which creates greater scheduling flexibility, reduces costs, enables targeted interventions for at-risk patients, and helps organizations transform to value-based care.

• 노쇼(No-Shows) 예측

  No-show patients are costly for providers and payers. can also be costly for patients because skipping medical appointments can lead to untreated medical conditions and adverse health outcomes. DataRobot can help predict which patients are most likely to be no-shows, equipping decision-makers with the information they need to rearrange schedules, craft interventions, and proactively engage patients. DataRobot can also aid in determining the probability of non-adherence and whether patients will respond to outreach efforts.