All day long, physicians perform critical cognitive activities such as assessing an individual’s risk of developing a medical condition, diagnosing the probable disease underlying a person's complaints, selecting the best therapy for treating a patient, and forecasting the course of a chronic condition in an individual. The better these activities are performed; the better clinical decisions and outcomes are likely to be for both the individual and society as a whole. However, the modern era of medicine, characterized by rapid technological advancements, increased regulatory demands, and evolving healthcare systems, has introduced significant cognitive burdens for physicians. To perform consistently at a high level, the physician requires up- to-date and relevant clinical information, comprehensive access to the patient's clinical data, robust decision-making abilities, and efficient documentation skills. Increasingly, physicians are experiencing burnout, overburdened by the cognitive demands of their daily routine that was manageable, enjoyable, and satisfying only a few decades ago. So, what has changed in the intervening years? Let’s have a look.
Furthermore, the traditional structure of medical knowledge, in which diseases are classified based on histological differences (histology refers to the appearance of tissues under a microscope), which has been the way medicine has been taught for over a century, is giving way to a more fine-grained classification of disease based on molecular differences (such as differences in proteins and the genome) and functional differences (such as how diseases behave and respond to therapies). For example, there are four histological types of breast cancer based on the microscopic appearance of the cancer cells. However, there are at least four molecular types of breast cancer based on the presence or absence of three molecular receptors known to fuel most breast cancers and four functional types based on how well the cancer responds to chemotherapy.
The classification of breast cancer into molecular and functional types advances precision medicine by tailoring therapies and predicting outcomes accurately. Future advances in molecular and functional understanding of breast cancer will increase the number of types of this disease. It is increasingly impossible for a physician to digest, remember, and keep themselves up to date on the vast amount of new information about a disease’s molecular and functional characteristics. This is the problem of knowledge overload.
As another example of rapid growth, the number of distinct medications available today compared to a century ago has increased exponentially, reflecting the tremendous advances in medical science and technology. Around the 1920s, the pharmacopeia was relatively limited, primarily comprising naturally derived compounds and a few synthetically produced drugs. In today’s pharmacopeia, most drugs are synthetically produced, and increasingly, medications for rare or complex conditions, which were untreatable a century ago, are now available. This growth has improved life expectancy and quality of life, but it also poses a significant challenge for physicians, as they are required to keep abreast of a vast and ever-expanding pharmacopeia. With each new medication, it is necessary to understand its mechanism of action, dosing, side effects, contraindications, and interactions with other drugs. This complexity is compounded when considering the variations of medication effects due to patient age, weight, liver and kidney function, and coexisting diseases. The more medications a patient is prescribed, the greater the risk of drug interactions and adverse side effects. Advances in genomics mean that some drugs have different effectiveness or have different side effects based on the genome. This adds another layer of complexity to prescribing decisions. Thus, the burden of keeping current with an ever-expanding pharmacopeia remains a significant aspect of modern medicine.
The medical knowledge and practice landscape has dramatically transformed over the past several decades. From the relatively static Galenic era to the dynamic and rapidly evolving modern era, physicians face the challenge of keeping pace with an ever-increasing volume and complexity of knowledge. This is evident in the shift from histological to molecular and functional disease classification, offering more precise treatment but also necessitating a deeper, more nuanced understanding of medicine. The exponential increase in the number of medications, coupled with advances in personalized medicine and genomics, further adds to the complexity of modern medicine. As medical knowledge continues to grow at an unprecedented rate, it becomes imperative to develop more efficient educational, informational, and decision-support tools. These tools are essential to assist physicians in staying abreast of the latest developments and effectively applying this vast knowledge to patient care.
This shift towards data-intensive healthcare has implications for both patient care and physician workload. While the availability of comprehensive data can enhance the accuracy of diagnoses and the personalization of treatments, it also significantly burdens physicians. Physicians must sift through vast amounts of information, which can be time-consuming and mentally taxing. This challenge is compounded by not all data being equally relevant or presented in an easily digestible format, making it difficult to quickly extract critical insights relevant to a patient's current clinical condition.
Furthermore, the integration and interoperability of different clinical data systems are an ongoing challenge. Data from various sources, such as laboratory results, imaging studies, and notes from different specialists, often reside in separate systems or sections within an EHR, requiring additional effort to compile and interpret. This fragmentation leads to information overload and missing critical patient data. Moreover, the reliance on EHRs and digital data has led to alert fatigue, where the high volume of alerts and reminders generated by these systems is overwhelming, causing essential notifications to be missed.
The impact of this data deluge extends beyond the individual physician to the broader healthcare system. It necessitates a reevaluation of workflows and the development of more efficient data management tools. There is a growing recognition of the need for advanced data analytics, artificial intelligence, and machine learning algorithms to synthesize and prioritize data, aiding clinical decision-making and reducing cognitive overload. While the exponential increase in patient data offers unprecedented opportunities for improving clinical care, it also presents significant challenges that must be addressed to optimize the use of this information. These challenges underscore the need for continued innovation in healthcare technology and processes to ensure that the deluge of data enhances rather than hinders patient care.
Many EHR systems are complex and not user-friendly, resulting in a steep learning curve. This complexity results in extended documentation times as physicians navigate through various interfaces. Some EHRs rely heavily on templates that may not capture the uniqueness of each patient encounter. Physicians thus spend additional time customizing templates or selecting appropriate options during documentation. Lack of interoperability between different EHR systems leads to duplicated efforts as physicians re-document or reconcile patient data from various sources. The complexity of the United States’ medical insurance system has resulted in massive regulatory and billing requirements that necessitate extensive documentation, coding, and billing.
The introduction of medical scribes, who translate and document information from patient encounters, was intended to alleviate the documentation burden on physicians. In recent times, digital scribes have been developed that utilize natural language processing and speech recognition to autonomously record and capture the verbal components of patient encounters. Future human-computer interaction technologies, including ambient listening and seeing, speech and gesture recognition, and speech and gesture recognition, will create autonomous digital scribe systems. And future systems will enable the transition from interacting with a standalone computer to speaking in an intelligent room, where the environment functions as the automated scribe.
Addressing the challenges associated with documentation in medicine requires a multi-faceted approach involving improvements in technology, training, and a reevaluation of documentation requirements to strike a balance between compliance and efficiency.
In summary, today’s physician is overwhelmed at the point of care with the demands of information gathering, including pertinent medical knowledge and patient data, which are exacerbated by the demands of extended documentation, including scribing, ordering, and billing. A modern airplane’s glass cockpit, which provides comprehensive and intuitive information about the aircraft’s status and performance, has been critical in improving situational awareness, allowing pilots to focus on threat assessment and decision-making. Inspired by the principles of a glass cockpit, better
integration of information gathering and scribing in a simple, intelligent interface in EHR systems can greatly improve the efficiency, accuracy, and performance of physicians.