Improving Patient Safety: How Data Can Inform Risk Management

Using healthcare data to identify vulnerabilities to patient safety helps inform organizational decision-making, leading to changes that create better patient outcomes. However, data is often siloed, and it can be difficult to combine data from many disparate sources to achieve a holistic view. Despite these challenges, there are ways to start aggregating information to reveal the bigger picture.

The Challenges of Consistent Data Collection

To identify data patterns and trends, organizations need to integrate data from multiple sources. However, this can be complex because the data is stored in different systems. For example, information from incident reports, root cause analyses, peer reviews, and patient complaints may be gathered and maintained separately, and the terms used to classify issues may not be consistent. Peer review protection issues may also come into play.

As an example, compare how data from incident reports and root cause analyses are used:

• Incident reports are often the first indication of an incident or risk that could become a claim, and these reports are a valuable source of data that can be used to better understand, detect, and prevent healthcare adverse events. However, limitations often prevent the full value of this data from being realized. For example, categorizations are often not broadly developed to identify trends. Low reporting rates can also hinder the utility of this data. While near misses can provide a wealth of information, these events do not always receive the reporting and scrutiny they deserve.
• Root cause analyses that occur in the aftermath of adverse events also provide critical data. These assessments identify root cause types and take a deep dive into the details leading up to the event. Most importantly, this information can drive action planning. These investigations are time consuming and may involve interviews with many stakeholders. Resulting actions from this work can be thwarted by internal influences, differing agendas, and the multifaceted nature of some events.

Although incident reports and root cause analyses are often viewed separately, an event that requires a root cause analysis may have started as an incident report. Categorizing events in incident reports with the same classifications used in root cause analyses is the first step toward aggregating data into a system that provides meaningful data.

The Value of a Common Taxonomy

Different patient safety systems often have different classifications for coding patient safety events. A comprehensive, mutual, and established set of risk categories can create a common language for conversations about patient safety, connect data from different sources, facilitate a comparative analysis, and help leadership focus on the big picture.

To realize these benefits, start looking at data in a systemic way:

• What is the case type code? In malpractice claims, the plaintiff’s allegation can identify the type of event. For other patient safety events, clear guidelines are needed to identify the case type accurately and consistently.
• What is the injury severity code? The same set of codes (for example, NAIC codes) should be used across all patient safety data.
• What are the risk management codes? These codes identify the broad safety concerns that may have contributed to injuries. Multiple risk management codes may be selected for an event since there is often more than one root cause.

Case in Point: How Data Collection Impacts Patient Care
The following case illustrates the real-life impact of data management on patient care.

A 26-year-old woman with a history of headaches received an adjustment from her chiropractor. Immediately after the manipulation, the patient developed slurred speech and was unable to raise her head or move her limbs. She was transferred to the emergency department, but by the time of arrival, her speech and neurological exam were normal. An MRI showed “suboptimal visualization of right vertebral artery,” but the examination was otherwise negative. The physician documented the results as normal, and the patient was diagnosed with a migraine and discharged.

The next day, the patient returned to the emergency department with complaints of difficulty swallowing and speaking. A lumbar puncture was negative, and a neurologist consult determined there were “no dangerous causes of her symptoms.”

Two days later, during an appointment with a neurologist, the patient continued to exhibit dysarthria and somnolence. She was advised to return for a follow-up visit and complete an outpatient MRI. She returned to the ED two days later with worsening neurological symptoms, and she was transferred to another facility where an MRI showed a pontine stroke, basilar artery occlusion, right vertebral artery occlusion, and vertebral artery dissection. She developed quadriparesis and locked in syndrome over the next 24 hours.

What went wrong?

According to the neurologist retained by the defense as an expert witness, the MRI request noting a neurological deficit following a chiropractic manipulation of the neck should have been a red flag for the radiologist, who should have made recommendations for additional images. The ER physician relied too heavily on the MRI and assumed the results were normal even though a critical area could not be well visualized. There was no indication that the radiologist and the ER physician were in communication. Furthermore, the neurologist who saw the patient in the office should have sent the patient for an emergent repeat MRI or CTA.

Diagnostic error is often a blind spot that may only be identified retrospectively, perhaps only after a medical malpractice claim has been filed. Data can help change this by improving communication and facilitating the identification of risk patterns.

Aggregating Data for Better Communication and Decision-Making

When disparate data shares a common taxonomy, everyone speaks the same language. This provides a valuable holistic view of patient safety that is not possible otherwise.

The following conditions enable progress:

• Organizational will and commitment.
• Innovative thinking and a willingness to break away from past paradigms.
• A common taxonomy applied to all data sets.
• Staff training on how to report events and near misses, with an emphasis on preventing future harm.
• Measures to ensure good data quality.
• Rules for how to code when there are multiple factors.
• Robust analytics.

Although tackling this type of change may seem overwhelming, you can start small. Pick one case type, such as patient falls and start there. Once you’ve established a common taxonomy, integrated data from multiple sources, and leveraged it to gain insights and make improvements, move on to another case type.

Data is often the missing link that risk managers don’t have. It provides evidence to confirm or negate suspicions about problem areas and empowers C-suite leaders with the facts they need to secure funding and commitment to enact change. With a complete and factual picture of what’s happening, healthcare organizations can improve patient safety and outcomes.

This article was, in part, based on the Coverys presentation “Why Data Matters to Manage Risk” presented by Maryann Small, MBA, Senior Director, Risk Management & Analytics, and Susanne Hess, RN, BSN, MBA, Senior Risk Consultant.