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 Safety 

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.

“The concept of safety as a box-checking enterprise, where we start and finish, is lethal to patients of the now and future”

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. 

From Risk Transfer to Risk Prevention

How NIXN is reshaping Insurance

The insurance industry has a history of providing financial relief to companies and individuals through disaster, illness, injury, and loss. However with growing access to data and technology, insurance has the potential to become much more.

The industry is approaching an inflection point, on one hand, companies continue to offer broader service offerings consuming more risk that daily, become more complex. On the other hand, the availability of data processing and technology should give greater insights and opportunities to influence behaviors and risk. This ability via NIXN allows the insurance industry to offer actionable advice and develop risk prevention services that open new markets, we call this “Risk Streaming”. Our world as we know it continues to change, the end user expects a tailored experience, with on demand service, and 24/7 connection, NIXN allows the insurer to offer this.

The Case for NIXN

Insurers need to build sustainable business cases in order to offer value to customers. The reality is NIXN’s business case is complex. Unfortunately, it’s not one of those endeavors that you can launch and have a return that is immediate.

NIXN is an investment. To make a decision to commit to telematics is committing to a new way of doing the entire insurance ecosystem.

The value is exponential based on the levels of maturity the stakeholder wishes to achieve with their program. The business case almost has to be looked at as an evolving artifact, with measured steps and managed expectations along the way. Depending on the program strategy that is adopted, a carrier may have to base the business case on incremental KPIs as its program matures. The first phase, for example, could focus on customer enrollment, risk mitigation and behavioral trends. Later in the program, KPIs could be introduced around retention and loss frequency. Further still, KPIs around underwriting profitability and segmentation may become more apparent.

Early on, a stakeholder may see value from a loss control and safety angle. Insurers around the world are investing in two approaches:

1. Real-time risk mitigation, where the detection of a risky situation triggers a reaction to prevent an accident.

2. Behavioral change enhancing the activity of the loss control team but also rewarding employees’ safe behaviors.

Moreover, as the program matures, the NIXN begins to provide value for pricing sophistication, risk selection and underwriting. To complete the value chain, NIXN can then have positive impacts on claims in terms of services, efficiency of the process and effectiveness of the decisions (including minimization of fraud and inflated claims). All these elements of the value creation equation need to be managed in order to deliver an adequate value for all the relevant stakeholders.

Automate Risk Management

Process > Outcome

Safety and Risk professionals across every industry from construction to general industry to insurance are scrambling to identify the most important measurements they can use to generate a sustainable and predictable safety performance.  Outcome metrics (Incident Rate, TRIR, EMR, DART, Safe / Unsafe, etc.) is where the industry has landed. Outcomes do matter, however if we only track outcomes we do not know if this based on a process, a system, a style of decision making, or just luck.  It is human nature to see an outcome and tell ourselves we as humans can control it, however, we all know when relying on other humans, that becomes impossible. The goal of every safety professional is to improve safety, quality, and costs, the metrics need to be grounded in evidence-based process measurements, that allow those same people to drive better outcomes.

Process Measures are Important

Process measures are the evidence-based best practices that represent a company’s efforts to systematize its improvement efforts. To illustrate this, I’ll use an injury prevention example.  Let’s say your organization needs to reduce falls from height injuries.  That is your outcome measure, you know your baseline rate, and you want to reduce it, but how are you going to actually drive improvement?

The answer is straightforward: by implementing and tracking the right process metrics.  Process measures in this example are the steps that should be preformed every time an employee is working from heights 4.5 feet or more.  Using NIXN preform risk assessments assessing and predicting risk of incident across all locations where employees work at heights 4.5 feet or more.

All employees at locations identified at 88 or more on NIXN risk scale would then go through a series of best practice preventative steps from engineering controls, proper planning, retraining, etc.  As each step within the preventative process is completed you can reapply NIXN assessments identifying the highest yielding preventative steps.  This process becomes scalable creating a sustainable solution that can last with the company.

Determining Root Cause to Solve the Problem

One of the greatest benefits of having this process metric data on hand is the ability to identify what is really causing the problem with employees working at heights.  The problem does not stem from the people.  It stems from your process.  In most organizations, however, the system of incident reporting does not recognize this fact.  The stigma attached to filling out the incident report “Employee Failed because he fell”.  Often, many near misses then go unreported because of organizations focus on outcome metrics.

“Really, it is the process that failed, not the person.”

By tracking process measures, you can pinpoint the root cause of the system’s failure.  You might find the employees are unable to maintain 100% tie off throughout job activity, or instead of being up on the platform scaffold would vastly reduce the need to be working from heights without adequate protection.

What you’re trying to do is move from a craftsmanship mentality to a system of production. And process metrics are the way that you do that. NIXN is the tool to systematically guarantee that employees perform in the safest manner, every time.

Process measures improve quality, cost, and safety by enabling organizations to reduce the amount of variation in job activity.  When the correlation of employee and company performance are high, it is optimal to establish process metrics, to reduce variation, standardize output, and drive optimal outcomes.

NIXN Allows Companies to Trust the Process

So why don’t more organizations have these kinds of systems in place? The answer is that they don’t have the infrastructure to handle outcome and process metrics. If they only have the resources to track one of these, they’ll choose outcome metrics, because those are the measures that must be submitted to regulatory bodies and insurance providers.

The reason organizations struggle to track all types of measures is because their analytics methodologies rely too much on manual work. When you don’t have the RIGHT technology infrastructure in place to automate the extraction and distribution of data, you end up having to do it all manually via spreadsheets and paper documents. You might be able to successfully use manual methods to track improvement for one process or even two, but once you get to the third, fourth, or fifth process, the manual work becomes unsustainable. And without continuous measurement, you won’t be able to sustain the gains you made in one process once you move on to the next.

That’s where NIXN with a robust and flexible analytics architecture comes in. NIXN forms the foundation for risk analytics by bringing all an organization’s data into a single source of organizational truth. This makes it possible to eliminate many of the manual process of compiling data and instead, delivers the automation you need to track a wide variety of outcome and process measures simultaneously. With NIXN, safety and risk analysts can then focus their time on discovering patterns in the data that will lead to understanding, insight, and ultimately action. But without NIXN, it will be very difficult for analysts to provide reliable and repeatable reports and in-depth analyses of areas that will reveal the best opportunities for improving outcomes.

Why NIXN?

In 2006 Chris Miranda founded MAC Safety Inc. , prior to founding MAC, Chris served as a safety manager / director for Sysco Foods for 8 Years.  Fast forward a decade MAC Safety Inc. was staffing large industrial jobs across the U.S. while also working with over 200 general industry clients ( Heavy Manufacturing, Pharmaceutical, Food Manufacturing, Healthcare, Transportation, etc.).  Along the way quantifying safety performance for companies was always the hardest hurdle.  Like all within the industry we relied  Incident Rates, TRIR’s, EMR’s, Near misses, Unsafe Actions,  to value a companies safety performance.  We were wrong.  The data we continued to review had no true predictive value on the future, or lended itself to current performance.  Instead, it was unrelated past events lacking measurement of process.

It was during this time that MAC Collectively realized there was a problem, no tool existed to measure safety, and no, not count outcomes, but truly measure safety as its taking place in the work environment.  Thus, NIXN was born.  We went to Carnegie Mellon recruited the best and brightest to help us solve what we as whole feel is the looming problem within the industry – Zero injuries for 7,838,724 days, does not ensure zero injuries today, nor does it give anyone within the company any sort of trend of performance. 

Paul DePodesta VP Cleveland Browns, Performance vs Outcomes:

“You know, it was right around this time that I ended up taking a weekend in Las Vegas, which, let’s be honest, is as good a place as any to have a philosophical epiphany. I was sitting there Friday night, I was playing blackjack and the casino was absolutely packed.  I was sitting over on the third base side of the table and the player sitting on the first base side, he was just playing terribly. I mean losing money hand over fist, and it seemed like, you know, every 15 minutes he was dipping into his pocket for more cash. And one particular hand, the dealer dealt first two cards- 17, she basically passed right over him as she dealt the next round until he stopped the dealer, and he said, no, no dealer, I want to hit. She paused, I think almost feeling sorry for him. And she said, sir, are you sure? And he said, absolutely, I want to hit. She produced the card and sure enough. It’s a four, right? The place goes crazy. I mean high fives all around. Everyone hooting and hollering and the dealer with total sincerity looked at the player and said, well, a nice hit.  I thought to myself, nice hit. I mean, just because it worked, it didn’t justify the decision. I couldn’t get this out of my head. I basically spent the rest of the weekend wandering around the casino, largely because I lost all my money playing blackjack, sort of wandering around thinking about how the casino works.  And what struck me was that while the casino is certainly concerned with outcomes, I mean after all, they’re there to make money. The way they achieve those outcomes is with an absolute laser like focus on process. And it’s not just the rules on the table, games that stack the odds in their favor.  It’s things like, like the carpeting, the lighting, the way you have to navigate through the property, all the way down to that hulking pit boss standing over you at the table. It’s all about process. And they believe that if they’re vigilant about sticking to their process, that in the long run they’re going to win.   And in case you haven’t noticed, you know, they normally do. I started thinking about how we might be able to take a similar mindset and bring it into baseball. So years later I was sharing this story with a friend of mine. He turned me onto a book called winning decisions. And then there was a very simple matrix of process and outcome, each one being either good or bad.  So in the upper left hand corner, it was good process, good outcome. Right? They called this deserved success. This is where we all want to be. It’s where most great companies are. It’s where most championship teams are. In fact, it’s where most great individual players are. You know, think Tom Brady, right?  He has an incredible work ethic. He watches, uh, an incredible amount of film. He has a relentless diet. He’s always prepared, right? He’s always prepared, and more often than not, he’s successful, right? Good process, good outcome.To the right you have good process, bad outcome. Right.  This can be a tough place to be. This is a bad break. This would happen to a casino when a player hits on 17 and manages to get her four, or what happens to Tom Brady when he throws the perfect pass, but it goes through the hands of his receiver and ends up as an interception. So the question is, when we face those situations, what do we do? You know, do we indict our process? You know, if Tom Brady throws an interception, should he go back and change his entire preparation routine. No. You know, he has a good process, but this can be a difficult place to be because if you’re not as sure about your process, you know, people will look at the outcome and say, well, geez, that didn’t work.  You know, you’ve got to change something up. So this can be a really tough place to be. The tougher place to be is down a level, which is bad process, good outcome, right? Player hits on 17 and manages to get a four. You know, cause oftentimes we do believe that the outcome justifies the process.  So if we get a good outcome, just one time, you know, we tend to repeat the process that got us there. You know, it’s why lottery winners continue to buy lottery tickets afterward. But what ends up happening is if we repeat that same bad process over and over and over again, we’ll just have basically a string of losses until maybe finally we get frustrated that we can’t get that same good outcome we got once upon a time. And then lastly, there was bad process, bad outcome, uh, which the authors called poetic justice, which seems appropriate.”

Paul DePodesta was the Asst. General Manager for the Oakland A’s, and many say the reason why Billy Beane’s data infused methods worked.  Like many safety professionals DePodesta lack resources ($) and found a unique way to win – Laser focus on the process.  Thats NIXN in a nut shell enabling users to measure real time process and receive quantified results in real time.  The resulting data sheds light on performance, how well your internal processes are working independent of the outcome and where change needs to occur.

Like most Moneyball, big data, and Paul DePodesta have inspired the MAC team to think deeper into what we collect, how we collect it, and how we then turn that into tangible data points that allow CEO’s, Shareholders, Site owners, Insurance carriers to assess safety, and align with company initiatives. We often talk internally at MAC about how safety professionals are like scouts for a MLB organization, experienced, have played, spend a majority of time in the field getting to know the players, and know the tangibles to look for. The best scouts today know how to quantify those tangibles into real data points upper management can use to make an informed decision.

NIXN disposes of the burden that historical safety data places on us.  We can’t forget that bad accident or the luck we had with a near miss.  Instead, NIXN focuses on the expected value with incidents.  The software examines your actual activity level data in real-time (not history) to calculate the expected value of an incident, injury, and total risk.  This is scientific, not anecdotal.  This is scalable, repeatable, and more importantly, it cuts through luck and bad breaks.  NIXN is the next generation of your safety program, because it makes expected values of risk and injuries visible and predictive.