In ensuring that EHRs serve future health care needs, it also falls on developers to think about how to “build humanity into our systems.”12 There are a few examples of this including, empathetic chatbots that respond with “I’m sorry that this has happened to you” instead of “Thank you for your feedback.”13 Another solution is allowing patients to record their names to be stored in the EHR to help clinicians pronounce their patients’ names properly, thus helping health care professionals practice culturally humble care and rebuild patient trust.14 Ensuring patient portals are available in different languages to improve equitable access to patient health information is another potential solution.
Approaching EHRs with human- and equity-centered design approaches could lead to a host of improved outcomes. A few of the improvements include, reduced time spent documenting, a better understanding of the patient’s medical history, a stronger patient/clinician relationship, less clinician burnout, more engaged health care consumers, and less stress in sometimes difficult health care interactions.
Interviewees also noted that a focus on prevention and wellness will be essential moving forward. As John Glaser writes, “the electronic health record must transition from an emphasis on a person’s medical record to an emphasis on a person’s plan for health.”15 Rather than recording what a patient is encountering, the EHR should help the clinician plan a course of action to improve the patient’s health or keep them healthy. This could include a library of care plans, personalized algorithms, care team support, interoperability, decision support, and analytics, and support the transition to value-based care. A person with well-managed diabetes, for example, would have a different care plan than that of a person with diabetes who needs more support. Survey respondents seemed keen on this idea as two-thirds of respondents are looking to enhance clinical decision support and advanced diagnostics for their clinicians in the next three to five years. In addition, 50% of respondents plan to invest in advanced analytics and machine learning that suggest a course of treatment and automate some of the steps.