Effectiveness of Electronic Health Record-Based Interventions for Improving Follow-Up in Primary Care

Effectiveness of Electronic Health Record-Based Interventions for Improving Follow-Up in Primary Care

Diagnostic delays in ambulatory care are often due to breakdowns of related care processes. Electronic systems can improve follow-up and reduce delays by detecting missed appointments or incomplete procedures so that patients are called back to conduct timely investigations when appropriate. To achieve high standards of patient safety in cancer diagnosis, the investigators not only need to use information technology appropriately but also improve the processes, policies, and procedures of monitoring, communication, and coordination of care.

Given the importance of cancer-related diagnostic delays in ambulatory care, the investigators need effective methods to detect them, understand their causes, and intervene to reduce them. Manual techniques to detect these delays, such as spontaneous reporting and random chart reviews, have limited effectiveness. Our proposed study focuses on testing methods to proactively identify delays using certain "triggers" as they occur and intervene in a timely manner.

The goal of this proposal is to demonstrate and test methods by which large health care systems can efficiently identify cancer patients who are more likely to experience diagnostic delays and pre-emptively rectify these delays. This study will build upon tools developed in our recent work (Aim1, prior IRB Protocol Number: H-23801) and test their effectiveness to identify patients at risk of experiencing delays in cancer diagnosis followed by an intervention that the investigators hypothesize will reduce these delays.

This is Aim 2 (for which the investigators are seeking approval) is the final Aim of this proposal. Aim 1 was approved under Protocol Number: H-23801.

In Aim 2 the investigators will determine the effectiveness of an IT-based intervention (consisting of data mining using triggers tested in Aim 1 followed by targeted electronic communication and surveillance techniques) to facilitate cancer diagnosis as compared with usual care (no use of trigger or electronic communication and surveillance). Hypothesis 1: The time from first appearance of a diagnostic clue to follow-up action (e.g. colonoscopy performance after a positive FOBT) will be significantly less in the intervention arm than in usual care. Hypothesis 2: The percentage of patients receiving timely follow-up care will be significantly more in the intervention arm than in usual care. To improve the generalizability of our findings to multiple ambulatory care environments, the investigators will conduct our research in two settings: an urban Veterans Affairs facility in Houston, Texas and a large primary care network in central Texas. These settings include internal medicine and family medicine, academic and nonacademic practices, and significant racial, gender, ethnic, age, urban/rural, and socioeconomic diversity. Our study addresses coordination and timeliness of care, both of which are priorities to achieve high quality care.

Hypothesis 3: Overall, the trigger will achieve a positive predictive value (PPV) of at least 50% in identifying delays in care. PPV is defined as the number of charts correctly identified with a delay in diagnostic evaluation, divided by the total number of charts identified by the trigger, and was deemed to be the approximately level necessary to avoid substantial contribution to provider alert fatigue.

Inclusion Criteria:

All primary care providers at both study sites who agree to be in the study. Intervention will be performed on those whose patients are electronically identified to have suspected cancer defined as presence of any predefined clue for cancer that is not followed-up in a timely manner. Three cancers are included; colorectal, lung and prostate and their clues include • chest x-imaging suspicious for malignancy • suspected or confirmed iron deficiency anemia • positive FOBT • hematochezia • abnormal PSA Patients will be selected from the data warehouse .

Exclusion Criteria:

Primary care providers who do not wish to be in the study.