A Race to Superior Performance
Healthcare organizations have worked hard to improve patient safety over the past several decades, however harm is still occurring at an unacceptable rate. Though the healthcare industry has made efforts (largely regulatory) to reduce patient harm, these measures are often not integrated with health system quality improvement efforts and may not result in fewer adverse events. This is largely because they fail to integrate regulatory data with improvement initiatives and, thus, to turn patient harm information into actionable insight.
Fully integrated clinical, cost, and operational data coupled with predictive analytics and machine learning are crucial to patient safety improvement. Tools that leverage this methodology will identify risk and suggest interventions across the continuum of care.
Avoiding patient harm is intrinsic to the work of healthcare professionals. Hippocrates, known as the Father of Modern Medicine, helped set this precedent when he said, “The physician must…have two special objects in view with regard to disease, namely, to do good or to do no harm.”
Contemporary medicine, however, still struggles to realize its primary mission. Today, researchers estimate that one in three hospitalized patients experiences preventable harm and over 400,000 individuals per year die from these injuries.
There is a gap in healthcare safety culture and the way health systems uses data (or think they use data) to understand patient harm and what to do about it. Much of the data collection is manual and not integrated with financial, operational, and other data, resulting in a fragmented approach to safety analytics that’s not actionable or predictive. Scores are recorded and boxes are checked, but the real work to make patients safer—closing the loop between information and action—is incomplete.
The status of patient safety moving forward, however, stands to improve. Despite the discouraging statistics above, in today’s era of data-driven healthcare, machine learning, and predictive analytics, the industry can turnaround decades of lost ground in patient safety and finally make much needed improvement in preventable errors.
Patient harm is defined as, “an injury that was caused by medical management (rather than the underlying disease) and that prolonged the hospitalization, produced a disability at the time of discharge, or both.” Adverse events can affect quality of life, delay treatment, lead to readmission, cause permanently disability, and more; at their worst, patients die.
Examples of patient harm include:
- Hospital-acquired infections (HAIs).
- Falls at the healthcare facility.
Illusion of Completeness
In the industry “There’s an illusion that we’ve worked on safety,” Health systems have failed to develop real insight into risks for patient harm, and to develop appropriate intervention protocols.
Quality and Patient Safety Also Impacts the Bottom Line
With the transition from fee-for-service (FFS) to value-based reimbursement, patient safety extends beyond patient welfare to increasingly impact a health system’s financial bottom line. Reimbursement will be tied to patient safety (and quality metrics). Health systems that aren’t currently engaged in driving down patient harm, or have high readmission rates, risk reduced reimbursement.
This is a significant shift in how hospitals are compensated for services; formerly, added services due to complications or readmissions made the organization money. As the industry moves toward safety-driven reimbursement, however, health systems risk not only loss of revenue if they don’t prioritize safety, but also the possibility that insurance companies will refuse to work with them.
Key Weaknesses in Patient Safety Today
Two significant studies concluded, respectively, that a) patient harm occurs frequent and is often caused by substandard care and b) adverse events are more likely the result of systemic flaw, rather than individual negligence.
Further, adverse events and preventable errors in healthcare are the third leading cause of death in the U.S.—proving that while the industry may have done work and performed research to improve patient safety, it’s made little to no progress.
If anything, the industry has regressed in the realm of patient safety. The system is inefficient: The industry repeatedly looks at the same HAIs when it needs to look at all-cause harm. Without a data-driven, all-cause approach to patient safety, history will continue to repeat itself.
Several issues stand out as weaknesses in the industry’s approach to patient safety:
Lack of an all-cause harm strategy: Health systems follow organizational—mostly governmental—mandates and specific metrics. When organizations take a siloed approach to patient safety, selecting a few harm initiatives, they may be putting their patients’ safety at risk. As a result, the industry loses a culture of always providing safety for the sake of staying safe in certain metrics.
Insufficient tracking of harm: The healthcare industry hasn’t developed an efficient way to track all-cause harm. Health systems lack internal automated surveillance and reporting systems. While reporting to regulatory agencies is required, these reports are not aligned with quality improvement initiatives. As a result, organizations tend to spend a lot of time on patient safety reporting, but have little time left for actual improvement; they have the data,(albeit in many cases a manual, time-intensive process), but there’s no follow-through with improvement initiatives based on that data. In addition, the current voluntary approach (in which frontline staff and physicians report adverse events at their discretion) is passable at best, but largely ineffective. Employees fear repercussions if they bring an issue forward; or they believe, based on experience, that no one will follow up, so there’s no use in reporting.
Lack of real-time harm data: Harm data from medical coders isn’t reliably accurate. The rates don’t always reflect accurate trends, and they’re retrospective, so there’s no real-time data to show near misses or opportunities to prevent harm.
Machine learning supports patient safety improvement with capabilities that are reactive, proactive, and fully integrated.
Reactive capabilities: With internal triggers, the safety tool reacts to potential harm by identifying risk and notifying frontline caregivers. This adds a layer of critical thinking to the tool.
Proactive capabilities: Once the tool determines risk within a patient set, predictive analytics identifies interventions to reduce or prevent harm. This proactive capability makes the information from risk triggers actionable—by suggesting intervention—and accessible—by putting otherwise hard-to-find procedures and protocols at the user’s fingertips. For example, the application might show that a patient is at risk for pressure ulcers and remind the caregiver to rotate them regularly and follow safe skincare practices to reduce risk.
Full integration capability: Because the tool is integrated across workflow tools, it enables improvement across the continuum of care. This allows improvement efforts to not be isolated and fragmented within departments—potentially impacting only a few patients—but implemented across an organization to impact many.