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Bridging gaps in care with artificial intelligence

Bridging Gaps In Care With Artificial Intelligence

As US payers and providers struggle with a fragmented healthcare system, patients with chronic conditions continue to experience gaps in their care. An AARP survey showed that a significant proportion of older adults experienced problems with their medical care, including medical errors (23%), poor communication (20%), readmission (15%), and lack of follow up (6%).

Medicaid gap in care issues

For Medicaid patients, the challenges were greater. The policy group MDRC studied the outcomes of six Chronic Illness Demonstration Projects (CIDP) that provided coordinated care to chronically ill Medicaid recipients in New York state.

They found challenges such as difficulty in obtaining timely information on hospitalization and emergency department visits; inaccurate contact information and residential instability that made it difficult to enroll recipients in services; and a lack of communication among providers. However, MDRC noted that coordinated care programs can achieve success through more in-person contact, targeting individuals at high risk of hospitalization, and focusing on managing transitions from hospital to home.

Whether for Medicare, Medicaid, or for patients with ACA insurance, there is room for improvement in gaps in care. A survey sponsored by the Council of Accountable Physician Practices found that coordination of care is improving, but for patients with multiple chronic conditions, the support they’re receiving is at the same level as healthier patients. Only 49% of these patients said their doctors are able to share information about their health or know their history before the appointment.

Using AI to improve gaps in care

For payers trying to bridge gaps in care, AI provides an answer. A study published in Digital Medicine used algorithms trained on medical, sociodemographic, and institutional variables to predict readmission, length of stay (LOS), and death within 48 to 72 hours. The researchers say they were able to generate models that made accurate predictions on readmission and LOS.

A similar study in the American Journal of Managed Care demonstrated that it is possible to use machine learning to generate a highly accurate model to predict inpatient and emergency department utilization using data on socioeconomic determinants of care. The study showed it is possible to predict patients’ risk of utilization without interacting with the patient or collecting information beyond the patient’s age, gender, race, and address.

AllazoHealth has a proven track record of using advanced AI to improve adherence, a key subset of gap in care that is heavily weighted by quality measures such as Star ratings. In a randomized control trial with Blue Cross Blue Shield North Carolina, our AI technology demonstrated a 5.5 times uplift in adherence compared with traditional programs.

 About AllazoHealth

AllazoHealth uses artificial intelligence to make a positive impact on medication adherence, gaps in care and therapy initiation. We help payers and PBMs increase the effectiveness of patient engagement programs that focus on quality outcomes, such as Star ratings, HEDIS scores, QRS ratings, and Medicaid quality measures.

Learn more about the impact of AllazoHealth's technology

OUR IMPACT

Improving the Effectiveness of Adherence Interventions by

5.45x

We worked alongside Blue Cross Blue Shield of North Carolina and their call center vendor to launch one of our biggest programs, working to improve adherence rates across their population of 104,392 Medicare Advantage Part D (MAPD) patients. We found that AllazoHealth targeted interventions accounted for 5.45 times the uplift in adherence compared to traditionally targeted interventions.