Guest post by Lawrence M. Fagan, Associate Director (retired), Biomedical Informatics Training Program, Stanford University.
As online resources proliferate, it becomes harder to figure out which resources—and which parts of those resources—will best answer patients’ questions about their medical care. Patients have access to multiple websites that summarize information about a particular disease, myriad patient communities, and many online research databases.
This resource overload problem isn’t new, however. As more and more data became available in the Intensive Care Units of the 1980s, it grew increasingly difficult to determine the most important measurement to track for optimal care.
In short, more information isn’t necessarily the best solution when it comes to answering patient questions.
After a career in informatics, I now moderate an online community for patients with a particular subtype of lymphoma. Many questions that arise in the group can easily be answered by reviewing existing online content. Health librarians are excellent resources to help guide patients to the correct resources and articles. However, some queries are less straightforward than others, such as: “What is the one thing you wished you knew before the procedure?” Rather than asking for a recitation of the steps in the procedure, this question is asking what was unexpected—or what step would have benefited from more patient or caregiver preparation. Correspondingly, these types of questions are hard to organize, store, and retrieve from patient-oriented databases.
Sometimes, the community of patients can recognize patterns that escape the notice of medical providers. For example, a lymphoma patient may complain of repeated sinus infections. It’s worth noting that patients often turn to their primary care provider to treat their sinus infections, and those visits may lead to antibiotic prescriptions. In this scenario, group members have pointed out the potentialbetween treatment with the drug Rituximab and a decrease in the body’s immunoglobulin levels. This connection leads to suggestions to explore an alternative treatment for chronic sinus infections (in this special context) using immunoglobulin replacement therapy rather than antibiotics.
Specialized online communities can also provide help with detailed care issues, including the treatment of side effects for uncommonly used drugs with which local healthcare providers might not be familiar.
Online communities can also suggest researching databases to answer patient questions.helps locate experimental treatments for specific medical conditions. Some community discussions about trials go beyond what’s included in the database. For instance, group members may discuss the optimal order of clinical trials in a specific medical area, based on an analysis of the inclusion criteria for the various trials. In addition, there are ancillary questions about trial logistics that aren’t found in the database, such as, “I live in the San Francisco area—is it feasible to participate in Trial X at City of Hope in Southern California?” Setting up comprehensive links between the clinical databases and discussions in patient communities would help patients access the answers to their questions more efficiently.
The answers to these specialized questions are often found in the archives of online communities or in the memories of group participants. Yet, it is not easy to find the right community for a particular medical problem, and in my understanding there is no central repository of links to online communities. Moreover, while many community links can be found in, static links to community websites often become stale, as sites may migrate locations over time. Some of the , for example, have migrated to . As patients find a community of interest, it is important that they determine whether the conversations are ongoing and whether the participants are knowledgeable and supportive. The Mayo Clinic offers a detailing the pros and cons of support groups.
Researchers have examined patient and clinician information needs for more than a quarter century. These models, however, have only rarely been incorporated into information retrieval systems. One successful example (aimed at providers) is the use of “” in PubMed, designed for searching the scientific literature. This brings us to a critical question: What would it take to reengineer the patient-oriented retrieval systems so that these focused queries drive most patient sites?
For now, we have communities of patients and dedicated professionals who are ready and willing to help point to the most useful answers.
Please note: The mention of any commercial or trade name is for information and does not imply endorsement on the part of the author or the National Library of Medicine.
Many thanks to Dave deBronkart, Janet Freeman-Daily, Robin Martinez, Tracie Tavel, and Roni Zeiger who reviewed earlier versions of this blog post.
Lawrence Fagan, MD, PhD, retired in 2012 from his role as Associate Director of the Stanford University Biomedical Informatics Training Program. He is a Fellow of the American College of Medical Informatics. His current interests are in patient engagement, precision health, and preventing medical errors.