As a healthcare provider, you are inundated with data like never before – from electronic health records (EHRs) to device readouts to claim transactions. You now have a tremendous opportunity to gain unparalleled intelligence, from your own data, to drive better patient outcomes and lower costs.
Ayasdi Care enables you to do just that. It uses advanced analytic algorithms and machine learning to tap into your own patient data to uncover and optimize clinical best practices for your hospitals and physicians.
Rapidly defining and refining optimal clinical pathways is just one example of what you can accomplish. Ayasdi Care will also help your clinicians and operations teams accurately assess readmission risk, evaluate the comparative effectiveness of drugs and devices, implement population management for the health of your patients, and more.
Finding best practices and systematizing them across your organization can be a herculean task. Effective clinical pathways hold the promise of delivering high quality care at lower and more predictable costs. However, clinical pathways are difficult to develop, maintain, and optimize. Typically, providers leverage peer-reviewed studies and expert physician knowledge to glean and tailor these pathways to best meet their care quality requirements. This approach, however, relies on consensus, across a limited number of clinicians, and on information gathered from an intentionally narrow scope addressed in clinical trials. Current approaches limit adoption and result in wasteful variations in care. By some estimates, 30% of all Medicare clinical care spending could be avoided without worsening health outcomes1.
Ayasdi Care represents a new data-driven approach to helping you develop, maintain and optimize clinical pathways. It uses advanced analytics to automatically discover optimal patient protocols - from your own data. This significantly improves physician acceptance. Within minutes, your clinicians can determine the clinical steps that deliver the best patient outcomes, at the lowest cost per patient. They can also model the impact of pathway changes on future patient outcomes, such as readmissions. And the pathways continue to adjust as new data arrives.
Some of the most innovative hospital systems use Ayasdi Care to develop data-driven clinical pathways to improve outcomes while lowering the cost of care. For instance, one hospital system estimates saving over $200 million in three years by accelerating the development of pathways four-fold and improving clinician adoption by 20% each year.
Ayasdi Care is designed to extract subtle signals from complex data, ensuring that all variables are taken into account when predicting an outcome. Your clinicians can then tailor these pathways based on their experience to deliver more personalized care. By taking both costs and outcomes into account, Chief Medical Officers and Operations teams can rest assured that their hospitals are delivering the highest quality of care at the lowest cost. And your patients and their families gain greater comfort knowing that they are receiving data-driven, personalized care.
1 John Wennberg et al. Tracking the Care of Patients with Severe Chronic Illness – The Dartmouth Atlas of Health Care 2008, The Dartmouth Institute for Health Policy and Clinical Practice.
Healthcare providers are increasingly looking for ways to monitor the health of their patients in outpatient settings to determine points of intervention to help reduce readmissions. The increase in adoption of wearable devices provides more data for closely monitoring chronic conditions and and planning timely interventions. Using Ayasdi’s solution, healthcare providers can monitor patients’ medication adherence and disease outcomes over time to determine the types of intervention that would be most beneficial. For instance, the Michael J. Fox Parkinson’s Foundation used Ayasdi Advanced Analytics to analyze data collected passively from smartphones carried by patients over a three-week period to precisely categorize them based on degrees of tremor detected. By monitoring patients using Ayasdi’s solution, clinicians can proactively intervene thereby reducing costly readmissions and emergency room visits.
Hospital care models rely on patient risk scores to help physicians achieve the best possible outcomes while optimizing the use of scarce resources. With Ayasdi’s solution, your clinicians can assess patient risk more effectively. For example, a major hospital had previously relied on their patients’ responses to a questionnaire to assess the potential risk of readmission. With Ayasdi’s solution, the hospital was able to compare predicted versus actual outcomes to identify flaws in both the questionnaire design and its interpretation. By incorporating the findings and continuously refining care triage models, hospitals are in a better position to deliver the best possible care at the lowest cost.
With the healthcare industry moving toward outcomes-based reimbursement, population health management initiatives are no longer optional. Ayasdi Care supports data-driven population health management initiatives by enabling better patient population risk assessment, care gaps identification, and patient intervention models. For example, a top hospital used Ayasdi Care to correlate patient sub-groups by treatment with care outcomes and costs, in one place, using data that was otherwise too complex and fragmented to analyze. By using Ayasdi Care to support population health management initiatives, your organization benefits from being able to better understand quality indicators and identify ways to improve care outcomes.
Healthcare providers are making reducing readmissions a strategic priority as they look to improve patient satisfaction and hospital ratings, avoid readmission penalties and reduce loss of revenue. Increasingly, readmission rates are being used as indicators of hospital quality, with unplanned, avoidable readmissions being considered as evidence of costly, sub-optimal care.
Innovative healthcare providers have turned to Ayasdi Care to help refine risk models to more accurately predict and prevent readmissions. Using Ayasdi Care, your clinicians can discover the root causes of readmissions that were not initially apparent. It helps them identify patients with a higher risk of readmission and proactively flag them for a post-discharge visit.
The percentage of a hospital’s claims that are “unclean” - rejected or denied – is a good indicator of the quality of its revenue cycle management. Some healthcare providers report as much as 25% of all claims as unclean in the collection process. Only a small fraction of this revenue is ever recoverable and the associated bad debt write-offs are increasing by 50% year-over-year. An improved understanding of the root cause of denials can prevent a substantial number of denied claims. Best-in-class organizations that leverage analytics and improved processes to target denials report less than 3% of lost revenue as a result of denials and less than 0.5% of lost revenue as a result of denials-related bad debt write offs.
With Ayasdi Care, your organization can correlate patient characteristics with steps in the scheduling, administrative, billing, and collections processes across facilities to isolate the root cause of denied claims. Healthcare organizations that define best practices to tackle the identified causes of denied claims can avoid the loss of approximately 5% of revenue, which is typically at stake from rejected or denied claims.
As healthcare becomes increasingly consumer-driven, we find providers looking for better ways of understanding their patient communities to precisely target their offerings. Using Ayasdi Care, providers can identify micro-clusters of patients by analyzing a wide variety of structured and unstructured patient and community data, including demographic, economic, and social media data. They can then tailor their service offerings, marketing and pricing to better fit the needs of these micro-clusters of patients. Additionally, by detecting the most subtle consumer patterns using Ayasdi Care, organizations can gain a better understanding of the reasons for patient retention and attrition. The uncovered insights help improve patient loyalty and hospital utilization.
Your organizations are increasingly being tasked with examining the effectiveness of drugs, medical devices, and procedures to ensure that your patients are receiving the highest quality of care at the lowest cost. Using Ayasdi Care, a leading healthcare system was able to analyze their physicians’ prescription patterns. They correlated high-prescribing physicians with a wide variety of factors, including patient care outcomes, length of stay, and average cost per treatment. As a result, this healthcare system identified high-prescribing physicians who were not achieving high-quality outcomes and put a plan in place to revise behavioral incentives. By using Ayasdi’s solution, your organization can not only establish best practices that reduce unnecessary primary care estimated to cost the U.S. nearly $7 billion each year, but also improve outcomes and patient satisfaction.
Advancements in patient stratification techniques can help medical researchers gain a deeper understanding of the molecular basis of diseases and further the practice of precision medicine. Using Ayasdi’s solution, your researchers can stratify patients to achieve more precise diagnoses, target treatments more effectively and, in some cases, prevent diseases from developing or progressing.
For instance, a physician can continuously evaluate a patient’s progress along a particular clinical pathway. Based on treatment results gathered along the way, Ayasdi’s solution automatically recommends personalized pathways for that particular patient.
With Ayasdi’s solution, your organization can quickly arrive at best practices for intervening early, targeting treatments more effectively, and preventing costly complications.