Right now, there’s a wealth of medical information available that can help physicians make better and more educated decisions using evidence-based medicine (EBM). The predicament lies in how to deliver this information to providers in an intelligent and efficient manner in order to create the maximum positive impact on patient outcomes. In a typical EBM situation today, the doctor will conduct online research to access evidence in the form of research studies and evidence-based clinical practice guidelines. However, there are a number of problems physicians encounter when using this traditional EBM approach:
1. Not enough time to read
It would require countless hours for doctors to read all relevant information associated with every diagnosis they make, by which time new evidence may have emerged, invalidating some of their prior findings. Even when data has already been collected, analyzed and summarized in systematic reviews or treatment guidelines, doctors still don’t have time to read everything for their field.
2. Difficulty in synthesizing an abundance of information
Doctors are only human. Like anyone, they may not only miss information they don’t have time to read, but also risk overlooking vital implications of evidence if they take everything at face value. It requires an enormous amount of time and training to appropriately interpret the array of complex factors impacting each study’s outcomes, as well as to understand how evidence relates to a given patient’s situation. With the traditional EBM process, each physician is responsible for making these judgments independently and correctly for each piece of evidence. However, most doctors aren’t research experts, and they shouldn’t need to be.
3. Evidence may not apply
Despite the aforementioned challenges, the greatest impediment to traditional EBM may be applying evidence properly to medical decision-making. Research studies and clinical guidelines usually present findings as broad, one-dimensional generalizations across populations. However, doctors don’t treat the “average” patient; they treat real people. To arrive at the right decision outcome, it’s essential to focus on the patient as an individual and consider all relevant issues. If a patient has a different demographic profile than the subjects of a study or trial, the study’s findings may not apply. Likewise, the impact of multimorbidity and drug interactions can be a life-or-death issue for patients, yet research studies tend not to cover it. Ultimately, it can be unsafe to act upon individual pieces of evidence without accounting for the complex network of interrelated elements on which human health depends.
In summary, for doctors to be able to practice EBM effectively, they must be able to quickly access evidence that is:
- Easily understandable and applicable
- Individualized for each patient
EBM at the Point of Care
Requiring each doctor to manually seek out, read, analyze and understand all relevant evidence to support every treatment decision is, essentially, impossible. A better solution is to automatically deliver the right information to physicians where it matters most – at the point of care. When medical expertise is combined with technological advances, practicing EBM at the point of care can become both possible and simple, and the best way to achieve this is through an electronic health records (EHR) system. However, not every EHR system is built to effectively address EBM at the point of care, so it’s helpful to know what characteristics to look for.
When expert doctors within a given specialty build an EHR system for their peers, they can code specialty-specific medical knowledge and evidence into the software directly. From there, they can update it as needed to keep the information current. In addition, they can build in the precise details that providers will need – for instance, which specific patient demographics are affected and how a given piece of information translates to treatment decisions and outcomes.
Finding evidence that is fully relevant to a specific patient requires detailed patient information, but doctors already input this data every time they perform an exam. When EHR software captures patient information as structured data rather than free text, it’s easy for the system to pinpoint which evidence applies to a specific patient based on all influencing factors.
According to webopedia.com, structured data refers to any data that resides in a fixed field within a record or file. This includes data contained in relational databases and spreadsheets. Unstructured data is all those things that can’t be so readily classified and fit into a neat box: photos and graphic images, videos, streaming instrument data, webpages, PDF files, PowerPoint presentations, emails, blog entries, wikis and word processing documents.
Having structured data allows the system to make relevant and best practice therapeutic options readily available to a doctor while the patient is still in the room. An EHR that does this particularly well is EMA™, which uses built-in protocols and algorithms to recommend individualized, evidence-based treatment options, such as medications, surveillance or procedures for each patient. For example, a doctor can optimize cancer surveillance with the help of the effective, cost-efficient surveillance guidelines embedded within EMA. These guidelines help the provider avoid overprescribing tests without missing vital opportunities for detection.
Moreover, physicians can go beyond simply viewing existing evidence with EMA – they can effortlessly access new evidence in real time by learning from their colleagues throughout the Network. Grand Rounds instantly analyzes and synthesizes clinical data from across the EHR system’s network of users in real time. This allows doctors to see which treatments are most commonly used for patients with particular characteristics, as well as which outcomes most commonly result from each treatment. With peer data at their fingertips, doctors no longer need to rely on the select audiences and limited factors tested in clinical trials. Instead, they are able to see how each treatment performs in the real world and on real patients similar to the ones they are treating.
In conclusion, when current evidence that is both clear and relevant is available for supporting educated treatment decisions at the point of care, doctors can truly utilize EBM rather than anecdotal medicine or rote memorization. Moreover, they can present information to patients and explain treatment options more easily, helping engage them in more meaningful and immediate conversations about personal health decisions. Ultimately, effective EBM at the point of care is a win/win situation. By minimizing costly practice pattern variations, physicians can save money while maximizing therapeutic potential and keeping patients out of harm’s way. Point-of-care EBM allows physicians to practice more efficiently and confidently, shifting the paradigm toward a new era where medicine focuses on outcomes, engages patients more and is supported by scientific data and expert consensus.