Human Factors Intervention to Reduce Risk in Primary Care of the Elderly (SAFE-C)

Human factors engineering literature makes clear that appropriate, well-designed and well-timed information improves decision making and can reduce mental workload. Data from a previous study showed that appropriate, well-designed and well-timed information is not present in many primary care encounters with elderly patients. This puts primary care physicians at risk of higher mental workload and poor decision making which can affect the quality and safety of care delivered to patients. Elderly patients are at particular risk because they are more likely to have more comorbidities, medications, and cognitive impairments.

Dr. Karsh and his research team will test an intervention to improve the performance of primary care physicians and, thus the safety of primary care of the elderly. The investigators will use a randomized experiment, with random assignment at the level of patient, to test and evaluate the intervention. The evaluation will involve 4 primary care clinics, with 4 primary care physicians per clinic. The investigators will collect data from 768 patient visits pre-intervention and 1536 patient visits during the intervention. Intervention patients will be randomly assigned to the intervention or care as usual.

The Intervention has two components:

Pre-visit care coordination:

• 5-7 days prior to a study patient's appointment with his/her doctor, the doctor's nurse/MA will call the study patient and collect pertinent clinical information about the patient using a data collection form the investigators call a Patient Overview Document or POD.
• The nurse/MA will ensure that any lab results, consultant reports, ER reports, imaging studies, etc., that will be needed by the physician are available to the doctor.

Team Meeting:

On the day of the patient's appointment and prior to the beginning of the clinic session, the nurse/MA will meet briefly with the doctor to jointly review the POD.

Hypotheses:

H1. Primary Care Physician (PCP): The intervention will increase situation awareness, reduce PCP mental workload, reduce PCP perceived likelihood of error, and improve PCP visit satisfaction. PCP efficiency, as measured by encounter problem density during a visit, will also improve.

H2. Patient: The intervention will improve patients' perceptions of their visits on a variety of AHRQ CAHPS measures, such as physician knowledge of patient history.

H3. Patient: The intervention will not impact the number or types of problems addressed during the visit.

H4. Clinic: The intervention will not affect visit RVUs