Contributors: Wren Keber, Lisa Soroka, and Z. Colette Edwards, WG'84, MD' 85
To learn more about Wren, Lisa, and Colette, click here.
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Promise of EHRs and Status Today
Electronic Health Records (EHRs) have long been heralded as a transformative technology in healthcare, promising to streamline operations, enhance patient care, collect data, and improve overall efficiency. The EHR journey began by aiming to digitize patient records, making them easily accessible and reducing reliance on paper-based systems with all its inherent limitations. Acceleration of EHR adoption was significantly influenced – both positively and negatively - by financial incentives triggered by the meaningful use of EHRs. This Centers for Medicare and Medicaid Services (CMS) program accelerated the pace of healthcare providers adopting and optimizing EHR technology solutions.
Today, the EHR landscape is dominated by major key players, including Epic, Cerner, and MEDITECH. These companies have established a strong market presence through mergers and consolidations, creating a hotly competitive environment that many believe is driving innovation and improvement in EHR systems. The dominance of a handful of vendors has led to a relatively standardized approach to EHR implementation, but it has also raised valid concerns around market concentration and the potential stifling of smaller, potentially innovative companies. Furthermore, consolidated market dynamics aside, there have been many challenges associated with truly realizing the disruptive potential of EHRs, which we will review in Part 2 of this series (Part 1).
Challenges in Today’s Systems
Despite the widely anticipated promise of EHRs, several challenges still persist, resulting in optimization and adoption delays. One of the primary issues is continued lagging optimization of EHR systems after initial implementation. Providers often resist adopting new technologies, principally due to increased workload and administrative burden that EHRs inevitably introduce. Instead of simplifying workflows, EHRs often add complexity, with multiple access points and complicated interfaces. Many providers come up with their own workarounds that can hamper leadership’s ability to understand how to make optimization improvements, such as truncating clinical notes/documentation and/or using third parties such as scribes.
The COVID-19 pandemic further exacerbated provider burnout, and many see EHRs as actively contributing to the strain rather than alleviating it. EHR system designs have become increasingly complex, necessitating both provider and administrative training that is continuous as well as change management. Encounter documentation within EHRs is often cumbersome, with numerous checkboxes and alerts that can overwhelm providers and frequently disrupt patient interaction. Free text fields, while offering flexibility, can lead to inconsistent data entry, potentially limiting the usability of the data downstream. This can disincentivize providers from writing out full notes, impacting the quality of patient records and usability of data that would otherwise be much more valuable. On the other hand, utilizing check boxes as the only data entry point may lead to lack of detail resulting in insufficient information to determine what exactly is going on with the patient (i.e., detailed narrative context) and also enables a clinician to much more easily check boxes without ever having actually performed actions like the physical examination, providing information regarding the risks of a procedure, etc.
The new generation of practitioners and patients have very distinctly different demands from EHR systems. Clinicians seek intuitive, easy-to-use systems that offer on-demand access to data in plain language they can easily understand. They want systems that integrate seamlessly into their workflows, reducing time spent on administrative tasks, thereby allowing them to focus more fully on patient care (the reason they went to medical school). On the other hand, a segment of the patient population wants seamless access to its health data through portals, online tools, and apps. They expect quick responses to inquiries, such as through online chat or messaging, and some also want the ability to manage their health information independently. Finding the middle ground between these two sets of divergent expectations is essential to patient satisfaction and high-quality care.
While automation and artificial intelligence (AI) hold great potential for enhancing EHR systems, there have been instances of AI failures from a provider perspective, highlighting the continued need for thorough testing and validation. For example, AI algorithms used for diagnostic purposes have sometimes produced inaccurate results, leading to misdiagnoses, inaccurate coding (often up-coding) and billing, and/or risk of patient harm. A recent study from Mount Sinai raised significant concerns with AI’s ability to accurately code patient charts. We will be examining AI and its associated disruption in a future installment of this series.
Keys to Success for EHR Disruption
1. Supporting Patients
Empowering patients is a crucial aspect of successful EHR disruption. Patient portals that provide access to their own records have the potential to enhance patient engagement and enable patents to avoid phone hold times. These portals should also offer opportunities for patients to update their financial information, request medication refills, and access test results and clinical notes.
Understanding the diverse needs of patients is essential; for instance, a 67-year-old male with a chronic condition might spend time reading through their records and using technology differently than a 29-year-old male in good health. The EHR system must cater to the needs of people of various ages, health conditions, and technological savviness.
2. Supporting Clinicians and Staff
EHR systems should be designed to capture data accurately and thereby substantiate billing and coding, thus help ensuring clinical documentation integrity (CDI). The goal is to make the tool transparent and non-disruptive, allowing providers to focus more fully on patient care. Automation of payer reporting and workflow processes can further enhance efficiency and reduce errors associated with manual interventions. One example is automating the collection of data for HEDIS, STARS, or other quality improvement programs to help identify and measure gaps in care and link the quality, efficiency, and evidence-based delivery of care in a way that the potential of a value-based reimbursement system can be fully realized.
A key question remains: How can EHRs be designed to enable clinicians to practice at the top of their license and staff to streamline workflows and work more seamlessly as a team? The EHR system should be built with the end-user in mind, so it aligns with the way providers and staff want to deliver care and support patients. It should also make it easy for practitioners to monitor preventive care and medication adherence, track the effectiveness of treatment plans over time, e.g., A1C, blood pressure, cholesterol, identify potential drug-drug interactions, stay up-to-date on new guidelines and standards of care, and practice in a manner that increases health equity. Those who create the system should be embedded into medical practices to really understand in a way that flows with the needs of patients, providers, and staff, instead of swimming upstream in a techno-centric way, and actually improve care rather than making it more difficult and costly to deliver.
3. Supporting Administration
EHR systems should also support administrative functions by providing robust analytics and reporting capabilities. This includes financial information for revenue, costing, service development, productivity reporting for provider compensation calculations, and automation of routine tasks.
4. Deploying AI That Works
While AI and automation offer significant benefits, they must be applied judiciously and ensure the algorithms utilized are free of bias. Understanding the specific use case and ensuring proper training and testing of machine learning models are essential.
Conclusion
We believe the true value of an EHR should be increased provider, staff, and patient satisfaction and capability during the course of day-to-day patient care. Quite simply, when a tool makes life easier and increases time spent with patients delivering care, it can ease administrative burden and hopefully decrease and/or alleviate provider burnout. When an EHR is implemented correctly, this objective can be realized. Downstream impacts and/or enhancements include improved data collection, enhanced coding, billing and collection, decreased and/or reduced clinical workflow friction and more informed clinical decision-making; all resulting in higher quality patient care and better outcomes.
Contact Wren at: [email protected]
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Contact Colette at: [email protected]