의료 산업

Healthcare is an industry in transition. Increasing pressure to deliver better care with lower costs is driving healthcare providers to invest in intelligent solutions that will inform their clinical, operational, and financial decisions. An AI-driven enterprise allows healthcare organizations to transform billions of data points into insights and predictions that improves quality of care and ultimately saves lives.

See how AI is transforming the healthcare industry.

개요 다운로드

의료 산업의 AI

Our mission is to help healthcare organizations, payers, and their partners use AI technology to help improve outcomes, streamline operations, identify financial risks, and retain their most valuable health consumers (patients, members, employees). DataRobot can work with you to turn your ever-increasing volumes of data into cutting edge insights and predictions that will drive cost saving clinical, operational, and financial decisions across your enterprise.

지불인

Be a leader in CAHPS, HEDIS, and Medicare Star quality ratings with superior healthcare analytics
Determine which members are at risk of leaving your health plan
리스크 조정 개선 및 최고의 타겟 기회 포착
사기성 청구 가능성 표시
정밀한 금융, 계리, 인수 모델을 구축하여 의료 비용, IBNR, MLR, 대규모 청구 예측, 보험료 산정 모델에 이용
분석을 활용하여 회원의 입원 기간 및 재입원 리스크 파악

의료기관

정확한 환자 재입원 리스크 모델 구축
수익 사이클 관리 및 수익 예측 최적화
Accurately forecast staffing needs with predictive analytics in healthcare
환자군 건강 상태를 적극적으로 관리하고 환자군을 리스크별로 정확하게 구분
분석을 활용하여 환자의 입원 기간이나 원내 감염 위험 판단
Be a leader in value-based healthcare

Vendors

Leverage predictive analytics to optimize your patient marketing campaigns, messaging, and call center operations
Increase renewals and reduce customer turnover while actively managing the efficiency of your sales force
제품 매출을 더욱 정확하게 예측
효과적인 환자/고객 메시징 구축
Optimize your patient/customer messaging
Integrate AI-driven insights into clinical solutions, patient engagement applications, and healthcare operating systems
뛰어난 분석으로 공급 및 수요망 계획의 리더

지불인
- Be a leader in CAHPS, HEDIS, and Medicare Star quality ratings with superior healthcare analytics
- Determine which members are at risk of leaving your health plan
- 리스크 조정 개선 및 최고의 타겟 기회 포착
- 사기성 청구 가능성 표시
- 정밀한 금융, 계리, 인수 모델을 구축하여 의료 비용, IBNR, MLR, 대규모 청구 예측, 보험료 산정 모델에 이용
- 분석을 활용하여 회원의 입원 기간 및 재입원 리스크 파악
의료기관
- 정확한 환자 재입원 리스크 모델 구축
- 수익 사이클 관리 및 수익 예측 최적화
- Accurately forecast staffing needs with predictive analytics in healthcare
- 환자군 건강 상태를 적극적으로 관리하고 환자군을 리스크별로 정확하게 구분
- 분석을 활용하여 환자의 입원 기간이나 원내 감염 위험 판단
- Be a leader in value-based healthcare
Vendors
- Leverage predictive analytics to optimize your patient marketing campaigns, messaging, and call center operations
- Increase renewals and reduce customer turnover while actively managing the efficiency of your sales force
- 제품 매출을 더욱 정확하게 예측
- 효과적인 환자/고객 메시징 구축
- Optimize your patient/customer messaging
- Integrate AI-driven insights into clinical solutions, patient engagement applications, and healthcare operating systems
- 뛰어난 분석으로 공급 및 수요망 계획의 리더

의료 산업의 고부가가치 분석 과제

There are a multitude of clinical, operational, and financial risk use cases for healthcare organizations, payers, and providers. The potential to dramatically reduce the cost of care while improving the quality of care is game-changing and achievable through an enterprise end-to-end AI strategy.
Check out all Healthcare use cases

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.

DataRobot이 제공하는 이점

CMIO(Chief Medical Informationtics Officers, 최고 의료정보 책임자)

Let DataRobot suggest the best model for each situation, saving your team time and effort trying and comparing every model. Use the enterprise AI platform to build many models at the same time it took to build one, increasing precision with more model granularity.
CAO(Chief Analytics Officers, 최고 분석 책임자)

With DataRobot’s enterprise AI platform, you acquire the productivity of a large data science team from a small one. Let DataRobot find the best models for you and use DataRobot’s simple deployment options to get them to market faster.
사업부 및 부서장

Tap into the expertise within the data that your healthcare organization already has. Enable business analysts and data analysts without formal data science training to build and use sophisticated models.
CDO(Chief Digital Officers, 최고 디지털 책임자)

코드 생성, Spark에 배포 및 API 기반 배포 기능이 포함되어 있는 DataRobot의 리스크 낮은 모델 배포 옵션을 사용하여 더욱 빠르게 모델을 프로덕션에 적용할 수 있습니다.
CIO(Chief Information Officers, 최고 정보 책임자)

The bottleneck in many healthcare organizations is no longer a lack of data, but rather plenty of data and not enough analytics staff to turn the data into insight. Democratize data science with DataRobot and watch the performance of your healthcare organization take off as the data analysis reveals opportunities for improvement.

이 웨비나 영상에서 Symphony Post Acute Network의 네이선은 병원 재입원 및 환자 낙상과 같은 문제에 대해 DataRobot이 어떻게 데이터 사이언스를 혁신하고 있는지 이야기합니다. 네이선이 "DataRobot의 플랫폼으로 제 일이 더 흥미롭고 재미있어졌고 더욱 정확하고 시의적절한 결과를 얻을 수 있었습니다. 이것은 마법과도 같은 플랫폼입니다!"라고 추천한 이유를 확인해 보십시오.

네이선 패트릭 테일러(Nathan Patrick Taylor)

CIO, Symphony Post Acute Network