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Five Ways to Leverage Analytics and Drive Quality Care

Clinician providing quality care to patient

Jonathan French, CPHIMS, SHIMSS

Analytics data can drive value-based care, reduce costs and improve quality care outcomes. And while many health systems have made significant investments into analytics, many organizations also struggle to identify the best method for inspiring clinicians to utilize the data.

Using our Davies Award winners as models, we’ve identified five ways to promote quality care by getting actionable data into the hands of clinicians.

1. Get Clinician Buy In

The first step for an organization is to establish the analytics platform as the single source of truth and measure data that impacts clinicians.

Start by focusing on widely used practices with clear value propositions in areas where compliance is necessary, including quality care measures reported to government agencies, accreditation bodies and value-based contracts.

By demonstrating a clear gap in care, clinical leadership can establish buy-in for measurement and corresponding workflow changes.

Memorial Hermann Health System, a two-time HIMSS Davies Award winner, implemented a sepsis program built on a standard order set and decision support guidance. In addition to measuring for reporting compliance, their informatics team developed a set of measures focused on three key questions, built around the Triple Aim designed to support the sepsis order set.

  1. How are clinicians responding to alerts and/or notifications?
  2. How are clinicians using the tools?
  3. Is care being provided to our patients in a timely manner?

Memorial Hermann made the decision to measure order set compliance and other measures that impact clinicians. For example, they measured the number of times alerts for sepsis fired to determine the risk of alert fatigue. The informatics team saw the number of alerts drop significantly as clinicians saw the importance of the data and started to change the way they delivered care.

2. Plan for Resistance to Change

Overcoming resistance to long-held methods of care delivery can be the most significant barrier to creating a robust, analytics-driven learning health system.

Physicians often leverage their clinical intuition for decision-making. Reviewing performance data often demonstrates that long-held methods of care delivery do not produce significant quality care improvements, and often result in significantly higher costs.

RELATED: Process Improvement Should Be Led by People, Supported by Technology

Here’s an example. Peer-reviewed research demonstrated that prescribing bivalirudin, a blood thinner used to treat patients receiving a percutaneous coronary intervention (PCI), was 300 times more costly with no discernable improvement in quality over a less expensive alternative. Despite this, physicians at UNC Health continued to prescribe bivalirudin.

Why was there resistance to adopting an appropriate model practice of using heparin, especially in a value-based care environment?

Catheter laboratory physicians had to see data to affirm that the change would not put patients at risk. According to UNC Health, key components to success were:

  1. Pilot a heparin-first strategy during PCI procedures
  2. Leverage a multi-disciplinary team including physician champions to assess the change and share their findings

Once the local data demonstrated heparin-first approaches were safe, UNC Health leadership launched an educational campaign to announce that heparin was now the standard of care for PCI at UNC Health and that a PCI dashboard would monitor compliance.

3. Gain Clinician Trust

Watch Davies recipient, talk with HIMSSTV about how it got all of its nursing and home care facilities documenting quality and utilization directly into its EHR to determine the best outcomes and give patients solutions to make care decisions.

There are several critical elements to gaining physician and other end-user buy in around quality care measures. Davies recipient, Open Door Family Medical Center, shared, “Nothing sinks a quality improvement project faster than having disengaged or mistrustful clinicians.” In order to build that trust, physicians and nurses must be confident that the selected measures are accurate reflections of the quality of care and that performance is attributed to the correct provider.

Data validation focused on the accuracy of the data and patient attribution—ensuring that a physician is not accountable for a patient when they are not the primary clinician responsible for the patient’s care—is a critical activity to establish buy in at the launch of an analytics-driven quality improvement project.

Several Davies recipients reported that challenges with patient attribution are a major barrier to success. Once Open Door validated their data and made clinicians a partner in the process and evaluation, clinicians “wanted that data and wanted to improve on their performance on those metrics,” shared Open Door chief medical officer, Darren Wu, MD. Once data was validated, Open Door established policies ensuring that providers were only accountable for the performance metrics associated with the patients on their panel.

Organizations varied with the methodology used to attribute patients. For Open Door, patients were attributed to their primary care provider (PCP). In order to be considered a patient’s PCP, the PCP had to see the patient at least twice within the last 12 months. Establishing a threshold where the organization can demonstrate repeated encounters with a single patient establishes credibility for the attribution methodology.

Second, required data elements for selected key performance indicators and measures must be accurately and efficiently gathered in the workflow. The EHR or other technology should use data elements already collected as part of the care process.

Typical EHR-enabled workflows utilize structured data fields for orders and clinical exclusion criteria. Making structured data fields easy to navigate is critical for success.

In addition, clinical quality and performance data must be deemed meaningful by clinicians. Clinicians must believe that the collected data will:

  • Identify gaps in care quality
  • Conduct workflow analysis and root cause analysis for performance outcomes
  • Trigger change management to adjust workflows and best practice guidance

4. Make Data Actionable

Once a strategy for assessment has buy in from the clinical team, the organization must select or create the appropriate data visualization interface to make data meaningful.

Data visualization tools must deliver information in as close to real time as possible. Early interventions generate better patient outcomes and delays in getting performance data to providers eliminate windows of opportunity to identify and address outliers and quality care gaps.

Clinicians should be able to parse out data at the patient level for any patient where the measure is attributed to their care. This allows the clinician to identify any mistakes in patient attribution. This also allows clinicians to quickly identify gaps in care.

Davies organizations we see achieving the most significant quality care improvements utilize technologies that promote a culture where clinicians access and review their own data on a regular basis.

5. Create Accountability

The opioid epidemic is one of the most significant public health crises facing North America. Ochsner Health System incorporated the use of analytics to showcase prescribing data of opioids and noted the importance of attributing the data to individual providers as a critical component of success.

Ochsner Health started their opioid stewardship program by creating a reporting process where their informatics team looked at the number of prescriptions for opioids in emergency rooms. “We began this in a blinded fashion,” said Ochsner Health Chief Medical Informatics Officer Todd Burstain.

After several months, Ochsner Health unblinded the data on the emergency department performance dashboards to show each individual provider compared to other providers for the number of prescriptions per day. Ochsner combined the dashboard with appropriate use guidance in the emergency department workflow, making it easy for a clinician to follow model quality care practices.

Ochsner then spread the dashboard throughout the system and saw a 40% reduction in opioid scripts in the first year following the unblinding of the data. To date, 26,000 fewer Ochsner patients have been prescribed opioids. The average dosing strength of scripts also decreased significantly.

RELATED: Developing an Opioid Abuse Program: Three Guidelines from Model Practices

The surprising benefit is that unblinding the data promotes collaboration in identifying best practices for quality care across the enterprise. “Being able to share practice patterns and understand cost/quality among peers resulted in positive change in behavior,” said Joel Schneider, MD, FACC, an interventional cardiologist with UNC REX.

In the ambulatory space, successful population health management was largely driven by immediate access to unblinded performance data. Care teams in small, federally qualified health centers like Open Door leveraged data visualization tools at the point of care to identify gaps to address during scheduled visits and to schedule visits for patients who were out of compliance.

In larger health systems, data visualization drives both improved performance and helps direct patients to new services to address gaps in care and improve population health management. Ochsner Health ambulatory providers could see their own performance as well as the performance of their peers in both their own clinic and across the system.

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Originally published April 29, 2019; updated August 19, 2019