Building a Data-Savvy Workforce

This entry is part 2 of 2 in the series Seeing the Big Picture in Big Data

One interesting and possibly new finding from this assessment is that employers believe there is a growing need for deep skills and understanding related to working effectively with data.  The authors commented: “…A difference was noted related to data management, data mining, data analytic, and similar skills and competencies. Twenty-six percent (26%) of employers indicated they would seek training for their staff related to data management and another 31% indicated they were seeking to hire these skills. So, more than half (57%) feel they need these skills in their organization. This unexpected finding illustrates the shifting nature of HIT workforce needs. This is expected to continue into the foreseeable future.”

Perhaps this goes without saying – there is no question we need a workforce with these competencies. But thinking a step or two further, coming full circle, we might recognize that we also need to reconsider the skills and competencies of our front line health care staff – primarily physicians and nurses – the key people generating and entering data into our systems, the initial creators of big data.

Hersh addresses this idea in a 2012 blog post 1, “Of course, those of us who work in clinical informatics know that gleaning value from clinical data is challenging. Indeed, those who have learned from implementation in the trenches may be best qualified to understand the limitations of their data. As I often say, documentation is not usually the highest priority for busy clinicians. Indeed, it is often what stands between a tired clinician at the end of the day and being able to go home for dinner. Clinical data also suffers from the lack of standards in structure and terminology of data, and it is often fragmented across different systems, both within and across different healthcare organizations.” More consistent use of data structure and content standards is certainly important, but mandating standard approaches for clinical documentation alone is not likely to get us where we need to go. We need to examine and change two other aspects of the data entry process.

One approach is reconsidering the models clinicians use to complete clinical documentation. For example, the Subjective, Objective, Assessment and Plan (SOAP) note described by Lawrence Weed nearly half a century ago is still in widespread use today. While this model was appropriate in a totally analog world using paper and pencil, in a digital world we may well need to develop, consider and adopt new models. John Halamka 2 proposes an interesting and novel approach – a major overhaul of the ways providers complete clinical documentation. He notes, “Imagine the following – the entire care team jointly authors a daily note for each patient using a novel application inspired by Wikipedia editing and Facebook communication. Data is captured using disease specific templates to ensure appropriate quality indicators are recorded. At the end of each day, the primary physician responsible for the patient’s care signs the note on behalf of the care team and the note is locked. Gone are the ‘chart wars,’ redundant statements, and miscommunication among team members. As the note is signed, key concepts described in the note are codified in SNOMED-CT. The SNOMED-CT concepts are reduced to a selection of suggested ICD-10 billing codes. A rules engine reports back to the clinician where additional detail is needed to justify each ICD-10 code i.e. a fracture must have the specifics of right/left, distal/proximal, open/closed, simple/comminuted.” Perhaps it is time we look at the ways IT enables redesign of documentation tasks, e.g., using social media and multi-authoring tools to refine clinical documentation for a digital world.

As the adage goes, “old habits die hard.” To achieve new clinical documentation models we must also consider new medical education models. Medical education must also more deeply reflect the data and information nature of health care delivery. We historically acculturate physicians as the “ship’s captain”, with their own memories serving as the primary knowledge repository. Today – and even more certainly in the future – this model is no longer sufficient. There is simply too much knowledge and too much data to keep in or rely on each physician’s working memory. Charles Freidman captured this idea in his vision of “Medical Education in an Era of Ubiquitous Information”. 3 He posits that by 2020, we will likely have “ubiquitous EHRs, health data that is fluid and computable, and a learning health system.” We will have medical practices “…supported by a knowledge cloud integrated with consumer – and care provider-facing systems”. In such a medical environment, a physician’s requisite competencies will follow three themes: “Knowing what you do and don’t know”, “Performing with support from the knowledge cloud” and “Evaluating and weighing evidence.” Friedman also suggests these new competencies will require that physicians have new and deep data understanding. For example, to retrieve useful answers, physicians using the knowledge cloud must phrase good questions as well as mine data effectively. Physicians skilled in evaluating and weighing evidence will become discriminating data users – learning to make decisions with vague and conflicting information. Friedman concludes by suggesting five ways to revise medical education curriculum. These include training in both the theory and practice of “decision analysis, data mining, meta-analysis, critical evaluation of the literature and evidence-based decision making under uncertainty.” In essence, Friedman argues that physicians must develop a much deeper and richer data understanding – appreciating what it is, its strengths and weaknesses, and how a variety of  people use that data for both individual patient care and transforming systemic health care delivery.

We see from the research studies that our future HIT workforce needs increasingly more data examination competency. At the same time, it is clear that effective examination depends on high quality data. A clinical workforce trained as Friedman proposes – where caregivers understand the importance of both data entry and examination – is a big part of responding to both these needs.

But it is only one part. If that workforce is going to make “meaningful use” of the mountain of data we now collect, we need to remember a critical aspect of goal achievement – to start with the end in mind. That means training people today for the way we want to practice medicine tomorrow. It also means making sure when this new wave of health care professionals arrives that the new HIT we specify, acquire and implement ensures that our “big data” supports our desired understanding of the big picture – improving individual patient care at a lower cost, affecting systemic change and enabling ongoing improvements into the foreseeable future.

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Posted on April 12th, 2013 in Innovating Health Care IT
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