Can Sharing Provider Documents Increase Value in Health Care?

Apple recently updated its Health app to display health records across multiple health centers. The screenshots and descriptions imply the company is accessing each health center’s patient portal to aggregate discrete data including allergies, medications, codified problems, immunizations, procedures and lab results. The user cannot modify the content or create a summary document to share with a new health care provider. Will distributing this technology more widely among health care providers and patients increase value in health care?

I don’t think so. SureScripts currently provides most clinicians to the medications recently filled by the patient within an electronic medical record so easy integration. A lack of consensus about what merits inclusion onto a patient’s problem list (suspicion of a diagnosis vs. response to a therapeutic trial vs. diagnostic test) limits trust among providers when reviewing problem lists created by others. Without tools to summarize and display information, a list of immunizations, lab results and procedures is likely to be ignored. Knowing that a patient has congestive heart failure with an ejection fraction of 15% with two hospitalizations in the last three months is much more informative than reading each data element separately across multiple screens.

So how might health care leverage the digitization of electronic medical records to increase value in health care? At least one vendor suggests that a longitudinal patient record could identify care delivery gaps and reduce duplicate testing and treatment strategies. Unfortunately, earlier efforts by the federal government to encourage more data sharing to help patients construct such a record have been poorly adopted in the marketplace (e.g.Blue Button).

Between our current state of discrete data element sharing and a longitudinal patient record could be a focus of using provider documentation and test results to help identify care gaps within and across care settings. For patients who have already interacted with the health care system, health information technology could provide support to help understand what tests or treatment failures merited additional follow-up.

Relying on discrete diagnoses and lab results may not accurately capture the patient’s clinical condition. Health care encompasses a variety of conditions and disease trajectories. Many conditions are self-limiting and do not require continued surveillance. Other findings are non-specific and may serve as a warning sign for a more concerning diagnosis.

Many clinicians do not take the time to specify the diagnosis to the level of detail that might help determine a more effective treatment or more accurate prognosis. “Stage 4 chronic kidney disease” implies a specific set of interventions and family conversations than “chronic kidney disease.” In addition, most health conditions do not have a diagnostic marker with 100% accuracy. A clinician three years ago may have an impression based on the patient’s clinical presentation that is completely debunked by a subsequent evaluation.Linking test result information or responses to therapeutic interventions can help improve a clinician’s confidence in the diagnosis specificity.

Even after accounting for the uncertainty around a single diagnosis, patients can also have other conditions that present in a similar fashion. Patients with congestive heart failure, chronic obstructive pulmonary disease and chronic kidney disease may present with shortness of breath to an exacerbation of any combination of these conditions. Without a gold standard, each clinician applies their own diagnostic bias without meaningful feedback to improve diagnostic acumen for future encounters.

Patient preferences should be explicitly captured and used as guideposts to determine next best steps in diagnosis or treatment. Advance care planning helps clinicians gauge what interventions would be appropriate based on the patient’s stated preferences. In some cases, patient preferences can help determine what diagnoses should be aggressively managed or what medication side effects should be avoided.

Time may alter a diagnosis, a treatment’s effectiveness and a patient’s preferences about disease management. When a clinician reviews a patient’s earlier health records, the historical information should serve as a starting point for the discussion about next steps to advance the patient’s care. Understanding a patient’s journey can help the clinician focus on those elements of care that may be most meaningful for the patient.

As high-deductible health plans become more prevalent, I expect patients and their families to compare prices, customer reviews and quality scores for elective medical services. Employers who subsidize health insurance for their employees should also see benefits from employees who make more informed health care purchasing decisions. To promote this type of consumer behavior, we in the health care system will have to:

  1. Provide patients with the information that led us to a particular diagnostic or therapeutic decision (test results, confidence in stated diagnosis, impression of patient’s values and preferences around the specific clinical condition within the context of the entire patient),
  2. Encourage more outcome-based metrics of care quality delivery (e.g., time to return-to-work after elective hip replacement),
  3. Encourage patients to seek out a second opinion for specific diagnoses or treatments that may not be clear, and
  4. Support patients if they make a decision to receive care somewhere else. Unlike buying a car or television, many patients may feel reluctant to leave a medical group or health system to save money or obtain a better experience. It is incumbent upon us to help patients find a health care provider or system that best meets their needs, even if that provider isn’t in our group.

I support any initiative that promotes health information sharing across health systems. To direct health information sharing toward higher-value care, it will be important to look beyond discrete data elements and use tools to extract information about diagnostic certainty, disease severity, decision making including patient preferences and next steps to improve the patient’s quality of life.