With the near universal implementation of electronic medical records (EMRs) in conjunction with enhanced data storage options, the time nears for real time data utilization in the clinical care process [1]. The subject of the increasing importance of data for health care is much talked and written about, but there is much less discussion regarding how data might specifically be used to drive and improve the individual clinician-patient interactions that accrue to formulate the process of health care. In other words, how could complete clinical decision support be implemented across the entire health care system? Big data is an increasing presence in health care, but data of all sizes are still underutilized. In those instances when they are used at all, they are used mainly in a retrospective analytic manner to analyze outcomes, processes, and costs. Currently, they do not dynamically drive clinical decision making in real time.

We have written on the need for the better use of intensive care unit data, noting that the development of data-based clinical decision support (CDS) tools would be one of the benefits of more comprehensive data capture [2]. Currently, the medical digital world comprises systems that are technically networked, but with data that are not systematically gathered, captured, or analyzed [3]. There are several studies that have demonstrated the potential applications and potential of capturing and analyzing clinical data [4,5]. In a more general response to this challenge, we describe a solution that combines the utilization of three fundamental components in real time: (1) big data, (2) search engines, and (3) EMRs. In particular, search engines are brilliant tools that we all utilize many times each day; however, they have not been systematically employed for the purpose of CDS, and they represent an overlooked resource.

The struggle to implement EMRs is finally coming to a mainly successful end in North America. However, the current generation of EMRs serves to digitalize information, but not to leverage it. The next step in the clinical digitization process should be the creation of a medical Internet equivalent that incorporates the rapid, powerful data search engine features that all current Web users employ. We refer to the real time incorporation of external data into the workflow as dynamic clinical data mining (DCDM) (Figure 1 shows this mining). This process will drive the design of the next generation of EMRs, and it will subsequently support the next required stage of the digital transformation process by turning medical practice into a data driven, logical, and optimized system. This care support system will provide users with the timely information that they require to make the increasingly complex decisions of medical practice.

We propose a system in which the knowledge gained from the care of individuals systematically contributes to the care of populations. The loop is closed when the richer data available in the population datasets is subsequently used in the care of individuals. DCDM would leverage the automatic crowd sourcing available in the form of population outcome analysis to formulate individualized diagnostic and therapeutic recommendations in real time. In other words, our viewpoint aligns with the Committee on a Framework for Developing a New Taxonomy of Disease, who advocate that “researchers and health care providers have access to very large sets of health and disease-related data linked to individual patients” in order to facilitate precision medicine [6]. To the Committee’s position, we would add our own that researchers and clinicians are already experienced with Internet search engines, so they would be comfortable with the identification of pertinent clinical information by accessing these large sets of data through a search engine metaphor. Currently, most clinical guidelines are generated by expert opinion based on experience and research findings such as randomized controlled trials [7]; DCDM would formulate the functional equivalent of personalized clinical guidelines.

While leading a team in the intensive care unit (ICU), one of us (LAC) experienced a difficult decision involving the resumption of anticoagulation in a patient with two mechanical heart valves. The patient was recovering from endocarditis complicated by brain abscesses. The team consulted local experts as well as the literature to guide them in weighing the risks and benefits of reinitiating anticoagulation, given the patient’s age, comorbidities, the specific bacteria involved, the number of mechanical valves, the extent and current status of the infection, etc. The information resources that were accessed provided only general recommendations that were obviously not tailored to the patient’s demographics and comorbidities, nor to the specifics of the clinical context. The majority of these recommendations were based on expert opinions or small clinical trials, and not on “gold-standard”, multi-center randomized controlled studies. The decision was made to restart anticoagulation cautiously, given the patient’s clinical stability, the absence of bleeding complications during the acute phase, and the lack of any planned surgical intervention. In fact, preparations were underway for discharge to a skilled nursing facility. Unfortunately, four days after reinitiation of anticoagulation, the patient suffered from a massive hemorrhage of one of the brain abscesses, prompting emergent hemicraniectomy. A DCDM system could have provided predictions of the harms and benefits of anticoagulation for such a complicated patient, and it would provide the previous outcomes associated with each treatment option to review in real time [8].

Uncertainties are not limited to complex scenarios, but occur with alarming frequency in all medical settings. For example, on a daily basis in the ICU, emergency department, or the operating room, clinicians target a desired blood pressure according to population-based guidelines. When hypotension ensues, the timing, mode, and extent of intervention to maintain that goal remain art rather than science. Given that interventions to raise blood pressure such as vasopressor therapy or fluids are associated with risk of harm if given even slightly in excess, it is crucial that the targeted blood pressure be personalized as much as possible. DCDM would add the knowledge gained from prior care of populations to the current local data specifics in order to formulate an approach that is optimal in terms of both the short-term goal as well as the long-term outcomes. For instance, DCDM could assist an ICU physician in choosing an intervention and its dose to treat shock, such that the intervention has the optimal effect on the short-term blood pressure profile and long-term mortality, length of stay, and/or eventual quality of life.

Other studies have explored similar themes. Certainly the application of logic and probability to medicine has been discussed for decades [9]. More recently, and more to the point of our discussion, a variety of commentators have called for a nationwide learning health system [10,11]. In 2011, Frankovich et al reported the case of a girl with lupus and potential thrombotic risk factors. To determine whether anticoagulation was appropriate, they used text searching to retrieve records of similar patients from their hospital’s EMR, followed by a focused manual review. They found that pediatric patients with lupus and these potential risk factors indeed had a higher risk of thrombosis than those without the risk factors, and they elected to start anticoagulation as a result [12]. The Query Health initiative, from the Office of the National Coordinator for Health Information Technology, intends to facilitate distributed queries, which can aggregate results from multiple organizations’ patient populations while preserving data security [13]. Similarly, the goal of the Strategic Health Information Technology Advanced Research Project on secondary use (SHARPn) is to standardize structured and unstructured EMR data to promote its reuse [14]. The open source Clinical Text Analysis and Knowledge Extraction System and the SHARPn program use the Unstructured Information Management Architecture, the same architecture that allowed the Watson system from International Business Machines to compete on the Jeopardy! television quiz show [15,16]. In general, search engines for unstructured text are seen as the first step, and implementation of “content analytics” is the next step to extract information, allow exploration, and to improve search [17].

CDS provides caregivers with information to improve the quality of their decision making, yet caregivers still do not have available a dynamic, comparative analysis between the current patient and all available data generated during clinical care. This analysis is individually tailored because it uses EMR data entered on one specific patient, yet it remains population-based because the analysis makes a comparison to population data, to identify similar clinical situations from the past, and to mine them for interventions and subsequent outcomes (as illustrated in point 5 in Figure 1). Thus, the clinician does not have to make a decision in isolation from what has been tried, observed, and documented by many colleagues in many other similar patients. In addition, the information provided would provide useful support to the process of patient-physician shared decision making [18]. This approach would interrogate data to suggest next step options and weigh the risks and benefits of a treatment or test for a specific patient, the Holy Grail of personalized medicine.

DCDM has its roots in the need for medical care to be more fully based on data. The universal collection of data would also present the additional advantages of providing future opportunities to formulate randomized registry trials, as well as for other directed data mining purposes [40]. DCDM would begin to transform the exigent data entries that clinicians perform on a daily basis into a real tool for clinical care. Decisions would be made on the basis of experience over vast populations, rather than solely on individual knowledge and experience. We propose the creation of a system that supports clinician decision makers so that their decisions can be as logical, transparent, and unambiguous as possible. DCDM would more gainfully employ the power of networked computers, search engines, and data storage advances to leverage the copious, but underused data entered into EMRs.