Top 3 Priorities for Health Data Governance, Management in 2019

Whether it’s a new clean-inbox policy or a membership to the gym, project manager (and serial resolution-breaker) knows that establishing a goal is only one small piece of the process.

Developing a detailed, realistic, and measurable plan for success is the hard part – and committing to the execution of that plan is typically even more difficult.

In the world of data analytics and data governance, the challenge is even greater.

Architecting and sustaining a future-proof big data management policy requires long-term buy in from a huge number of stakeholders, some of whom may be reluctant to add new processes to their existing workflows.

Convincing health IT users that the destination is worth the journey can be a very challenging task, especially when many clinicians and administrative staff members feel overwhelmed by interacting with their EHRs, practice management systems, and analytics reporting tools.

In an environment where frustration with digital systems is growing at almost the same rate as the volume of available big data for analytics, organizations will have to carefully choose their battles in order to produce maximum impact with minimum amounts of disruption.

Here are three suggestions for data analytics, HIM, and data governance priorities to embrace in 2019 as hospitals, health systems, and physician practices move further into the age of analytics.


Burnout among physicians and nurses is reaching epidemic levels at most healthcare organizations.

While stress, fatigue, and dissatisfaction in the workplace are attributable to multiple factors, the electronic health record and its documentation demands have taken most of the blame for the need to spend long hours hunched over the keyboard, chastising the computer for its many failures.

One recent survey found that 83 percent of physicians and 78 percent of RNs believe burnout is a moderate or serious problem at their organizations, with administrative burdens and unsustainable workloads ranking highly as drivers of unhappiness in the office.

Health IT developers, responding to pressure from their customers to make their products easier and more intuitive to use, are exploring a range of strategies to make data simpler to find and utilize.

Artificial intelligence is viewed as a particularly promising technique for developers to create products with the potential to reduce cognitive clutter, lend support to important clinical decisions, and streamline the process of getting data in and out of the EHR.

But much of the optimization process happens at the organizational level.

Provider groups have a number of choices they can make to tailor workflows to their particular needs, leaving system-level leaders with the challenge of ensuring that their processes and guidelines are solving problems instead of causing them.

In 2017, more than half of hospital executives responding to a survey from Dimensional Insight said that their organizations did not have an overarching data governance or data analytics plan in place. The lack of cohesive data strategy led to discrepancies in clinical reporting, conflict between departments, breakdowns in communication and culture, and unclear quality measurement processes.

The resulting lack of trust in data and analytics puts unnecessary stress on staff members, who may be receiving contradictory instructions or varying assessments of their performance, increasing the likelihood of their joining the ranks of the burned out and fed up.

comprehensive approach to governance and workflow optimization may be able to increase users’ ability to create trustworthy data from the beginning, reducing avoidable agitation when the numbers don’t add up on the back end.

Over the next twelve months, organizations with gaps in their governance processes should consider taking the following steps to ensure every member of the team is on the same page:

Identify pain points in the data input process

Cumbersome clicks and the need to navigate to multiple screens to input data can easily lead to unofficial shortcuts and abandonment of correct procedures.  Any optimization effort should begin with a thorough review of challenges and pain points at the user level to ensure that the right concerns are being addressed.  Leaders may find that their users’ idea of what’s problematic doesn’t always match up with their own.

Assess the data pipeline from creation to deletion

Data siloes are a major concern for analytics experts looking to craft accessible sets of standardized information.  Every time information moves, it creates a number of risks, such as translation errors or exposure to security flaws. Does health data flow seamlessly through the organization, or are there hiccups in the process?

Establish an enterprise-wide governance plan

As often as possible, different departments should share protocols and standards for creating, collecting, transmitting, and analyzing data.  Creating a single source of truth across the enterprise can help to eliminate confusion or contention about performance, risk, and rewards.  Establishing a single, organization-wide data governance plan will require executive leadership and committed participation from stakeholders at all levels.

Communicate and iterate collaboratively

Frustration and resentment thrive in environments that fail to communicate proactively and take multiple viewpoints into account.  Top-down decisions with few opportunities for end-users to share feedback can exacerbate stress and disengagement, increasing the burnout problem.  Appointing representatives from different areas of the organization, communicating transparently and often, and incorporating suggestions into the revision process can foster buy-in and commitment to change.


Big data analytics can support more informed decision making, but it can also lead to unintentional errors that put patient safety in jeopardy.

Typos in digital data or a misreading of a value can lead to medication errors, while failed alarms or human fatigue after too many alerts can cause serious delays in care or diagnostic errors.  Malfunctioning equipment

Patient safety in the digital environment consistently ranks among the industry’s top ongoing concerns, and may become even more of a hot-button issue as artificial intelligence begins to spur additional adoption of advanced clinical decision support (CDS) systems.

Investing in CDS tools that offer clear rationales for their suggestions – and offer transparency into what data is being used to fuel those recommendations – will help to ensure that clinicians feel confident in their analytics-based assistants.

This may prevent deviation from recommended clinical guidelines and create more trust in computer-generated diagnostic assistance.

Meanwhile, HIM professionals and EHR optimization experts should take a close look at how information is presented to clinicians at the point of care.

Low-value alerts can desensitize users to more important developments.  At the same time, letting users breeze though too many checkpoints unaided can result in lower quality careand potential safety concerns.

The ECRI Partnership for Heath IT Patient Safety places a high priority on designing health IT systems with effective communication features.

In a 2018 report, the industry group stressed the importance of adopting standards for critical tasks such as sharing data on abnormal results, noting that many organizations have differing thresholds for when a clinical indicator is out of range.

Collaborating effectively between laboratory test facilities, pharmacies, inpatient, and outpatient care settings can ensure that the quality of information stays high when data about serious conditions are shared across the care continuum.

Improved use of data standards can also help to automate reporting and alerting on patient deterioration in the inpatient and critical care settings.  Time-sensitive conditions, such as worsening surgical site infections, sepsis, or respiratory failure, require providers to have speedy access to patient data so they can take life-saving action.

Hospitals subject to financial penalties for patient safety mistakes and value-based reimbursement contracts are particularly advised to ensure their analytics tools are supporting their efforts to reduce preventable harm.

Organizations should also ensure that their clinical staff members receive regular training and education about patient safety concerns, and that working conditions are conducive to allowing staff to devote adequate attention to identifying and preventing potential concerns.

Healthcare leaders should actively work to create a culture of non-punitive reporting and problem solving that will encourage staff – and patients – to report their own mistakes, the errors of a colleague or a flaw in the health IT environment and come forward with suggestions for improvement.

Developing an open environment that strives for continuous improvement should be a top focus for patient safety leadership and the HIM department over the coming year.


Getting the right data to the right person at the right time is the goal of all data analytics initiatives, but unfortunately many organizations fall short on the delivery component.

Receiving a report is not the same as utilizing the data effectively, especially when the aforementioned issues of burnout and information overload are affecting clinicians.

Better visualizations and easier-to-digest reporting can help to ensure that potentially valuable data doesn’t end up in the trashcan.

From color-coded confidence intervals to interactive dashboards for financial performance metrics, the right visualization can engage users and help key information stick.

Visually-pleasing interfaces that borrow elements from consumer technologies can also speed up the learning process for staff members new to working with data.

To reduce the learning curve, dashboards and visualizations should be consistent in color, design, and use of units while offering the option to dive more deeply into key statistics for more advanced users.

Visualizations can improve the execution of a number of important processes, including:

Patient hand-offs and transitions of care

Changes in care settings, or even shift changes, can result in communication gaps, missed information, and lapses in care delivery.  Offering care teams the ability to create a color-coded checklist, for example, of high-priority items for each patient may be able to smooth the process of moving beds or handing off an individual when a new nurse comes on duty.

Risk scoring and risk stratification

Making a patient’s risk score readily available to a provider at a glance can help ensure that the individual receives the care most appropriate to his or her health status.  In the acute care setting, easy-to-read risk scores can prevent potentially serious complications such as acute kidney injuries.  In the primary care environment, visually striking risk stratification data can aid chronic disease management and proactive, preventive care.

Financial and operational analytics

In the world of finance, an organization is either in the black or in the red – phrases which immediately highlight the importance of good data visualizations and clear, unambiguous reporting.  When it comes to the complex day-to-day operations of a hospital or clinic, however, “good” and “bad” can be somewhat less clear.  Meaningful reporting, illustrated simply through charts and graphs, can help providers understand their operational performance and adjust their processes accordingly.

As the industry moves into 2019, providers will only be asked to synthesize more and more data sources to develop accurate diagnoses, treatment plans, and financial strategies.  Ensuring that this data is presented to key stakeholders in a meaningful, intuitive manner will be vital to making certain that information is absorbed and applied to pressing business problems and clinical concerns.

All three of these high priorities will work in concert to ensure that both providers and patients have the best possible experiences as they interact with the healthcare system.  Reducing burnout through better technologies and simpler reporting will help staff members prevent patient safety errors, creating a continuous loop of improvement to set organizations up for success in the year to come.

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