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

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.

The intervention will include activities such as electronic communication and surveillance that facilitate the care of patients experiencing delays. A trained chart reviewer will conduct chart reviews on trigger-positive patients to confirm they are at risk for care delays and this will be followed by an electronic and/or verbal communication to the provider. The intervention will be compared to usual care at both sites.

The usual care at MEDVAMC consists of providers using an advanced EHR and its notification system (the View Alert system) that immediately alerts providers about clinically significant events. The system relies primarily on computerized notification (alerts) displayed prominently through a "View Alert" window that is displayed in the EHR every time a provider signs on or switches between patient records. The system does not require providers to read alerts, and providers do have an option of ignoring the View Alert window to bypass it. At SWHS there is a navigation program for patients who have received a cancer diagnosis by tissue biopsy. However, currently there is no routine tracking of patients if they do not show for their scheduled appointments and tests at SWHS.

The intervention will include activities such as electronic communication and surveillance that facilitate the care of patients experiencing delays. A trained chart reviewer will conduct chart reviews on trigger-positive patients to confirm they are at risk for care delays and this will be followed by an electronic and/or verbal communication to the provider. The intervention will be compared to usual care at both sites.

Differences between the intervention and control groups (based on a Cox Proportional Hazards Survival Analysis) in median time to documented follow-up of a red flag (e.g., colonoscopy performance after positive FOBT) or of a deliberate decision by the treating provider not to take follow-up action. When less than 50% of patients in either group received diagnostic evaluation (ie, medians were not reached), the point at which 40% received diagnostic evaluation was compared instead.

The percentage of patients receiving timely follow-up care, as defined by action taken by provider within appropriate pre-defined time intervals for each diagnostic clue, in both intervention and control groups.

This is a descriptive sub-analysis looking only at cases with no follow-up at the end of the follow-up period. Specifically, out of the cases that never got follow-up, this represents the percent of that subsample that had no justification in the medical record for the lack of follow-up. This is based on manual chart reviews.

Subsequent diagnosis of nonmalignant neoplasia, cancer, or death, and treatments required as a result of new cancer diagnoses after a pre-specified interval.

Positive Predictive Values of each of the triggers in identifying patients with a true delay in diagnostic evaluation. Calculated as: percentage of patients identified as trigger positive that actually had a delay.

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.

Murphy DR, Wu L, Thomas EJ, Forjuoh SN, Meyer AN, Singh H. Electronic Trigger-Based Intervention to Reduce Delays in Diagnostic Evaluation for Cancer: A Cluster Randomized Controlled Trial. J Clin Oncol. 2015 Nov 1;33(31):3560-7. doi: 10.1200/JCO.2015.61.1301. Epub 2015 Aug 24.