Research Initiatives

PHS stimulates research initiatives in the following areas. Some initiatives represent new directions for ongoing collaborations while others are in every sense new. Contact us for more information about any of the initiatives or to be connected with the investigators.

The Burden of Systemic Lupus Erythematosus (SLE) in Israel

Disparities in incidence, prevalence, severity, and complications of systemic lupus erythematosus (SLE) have been demonstrated by race and ethnicity in the United States and abroad. In general, non-white populations are more likely to have SLE but there is substantial misclassification of race and ethnicity, particularly among people in the Middle East who may be labeled as “white” and other times as “other”. Work with Dr. Julia Simard in partnership with the Clalit Research Institute is focused on further examining groups often classified as either “white” or “other” using population-based data from Israel (specifically distinguishing between Sephardic Jews, Ashkenazi Jews, Ethiopian Jews, Muslim Arabs and more) to assess SLE epidemiology across these ethnic groups using Clalit’s valid, well

The research team plans to not only identify individuals both newly diagnosed and living with SLE, but to understand the disease by ethnic group examining medication use, clinical status, and associated comorbidities. They are also investigating renal manifestations because lupus nephritis is one of the most severe and common phenotypes. For more information on this project, contact Dr. Julia Simard.  


Can Targeting High-Risk Patients Reduce Readmission Rates? Evidence from Israel

In partnership with the Clalit Research Institute, Dr. Liran Einav has described the impact of a large intervention to reduce hospital readmission rates undertaken by the Clalit, the largest Israeli integrated healthcare system. Since 2012, the intervention flagged patients aged 65 and older with high readmission risk to providers, both upon admission and after discharge. Risk scores were based on patient-specific prior healthcare utilization. They found that the intervention reduced 30-day readmission rates by 5.9% among patients aged 65–70. Primary care post-discharge follow-up encounters were significantly expedited. The magnitude of the estimated effect peaked during the first two years, and it declined subsequently, after incentive payments by the Israeli Ministry of Health to organizations that reduce readmission rates were discontinued. Taken together, the evidence demonstrates that informing providers about patient risk in real time can improve care continuity and reduce hospital readmissions, and that maintaining such efforts on an ongoing basis is important to sustain their impact.


Additional studies of high-risk patients and tailored, effective health care utilization are in process, specifically investigations of cancer care, end-of-life care and emergency room visits. For more information on these projects, contact Dr. Liran Einav.


An Effort to Identify Familial Hypercholesterolemia Patients in the Databases of Clalit Health Services using Machine Learning Algorithms

Health care policies in the U.S. are undergoing a transformation towards value-based care with emphasis placed on risk stratification, disease identification, and preventive care. These approaches can reduce costs, increase efficiency and ultimately improve outcomes, by delivering the right care to the right people. Machine learning (ML) methods allow the use of structured and unstructured EMR data to create predictive models to more precisely address treatable conditions that have traditionally been underdiagnosed. A team working with Drs. Joshua Knowles and Nigam Shah under the FH Foundation′s FIND FH initiative applied these methods to developed a ML algorithm to identify potential familial hypercholesterolemia (FH) cases. FH is an underdiagnosed dominant genetic condition (~10% diagnosed in the United States) that has up to a 20-fold increased risk of coronary artery disease if untreated. The results of this initial study showed significant increases in the early identification of at-risk patients versus standard clinical diagnostic methods. Read the published article here.

Now, the team is translating this work to the Israeli population through a partnership with the Clalit Research Institute. At a “personal” and “local” level the team will be identifying individuals with FH that can be targeted for potentially life-altering intervention. Within the Stanford EHR database of 2 million patients and Clalit database of 4.4 million, there are potentially as many as 25,000 FH patients that will benefit from early, precision diagnosis and treatment.  For more information on this project, contact Elinor Briskin.