Early Warnings Demonstrate an Early Advance in the Use of Analytics to Improve Health Care

Posted on May 4, 2015 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

Early warning systems–such as the popular Modified Early Warning System (MEWS) used in many hospitals–form one of the first waves in the ocean of analytics we need to wash over our health care system. Eventually, health care will elegantly integrate medical device output, electronic patient records, research findings, software algorithms, and–yes, let us not forget–the clinician’s expertise in a timely intervention into patient care. Because early warning systems are more mature than many of the analytics that researchers are currently trying out, it’s useful to look at advances in early warning to see trends that can benefit the rest of health care as well.

I talked this week to Susan Niemeier, Chief Nursing Officer at CapsuleTech, a provider of medical device integration solutions. They sell (among other things) a bedside mobile clinical computer called the Neuron that collects, displays, and sends to the electronic medical record vital signs from medical devices: temperature, pulse, respiration, pulse oximetry, and so on. A recent enhancement called the Early Warning Scoring System (EWSS) adds an extra level of analytics that, according to Niemeier, can identify subtle signs of patient deterioration well before a critical event. It’s part of Capsule’s overarching aim to enable hospitals to do more with the massive amount of data generated by devices.

For more than 18 years, CapsuleTech provided bedside medical device connectivity products and services that captured patient vital signs and communicated that data to the hospital EMR. Rudimentary as this functionality may appear to people using automated systems in other industries, it was a welcome advance for nurses and doctors in hospitals. Formerly, according to Niemeier, nurses would scribble down on a scrap of paper or a napkin the vital signs they saw on the monitors. It might be a few hours before they could enter these into the record–and lots could go wrong in that time. Furthermore, the record was a simple repository, with no software observing trends or drawing conclusions.

Neuron 2 running Early Warning Scoring System

Neuron 2 running Early Warning Scoring System

So in addition to relieving the nurse of clerical work (along with likely errors that it entails), and enhancing workflow, the Neuron could make sure the record immediately reflected vital signs. Now the Neuron performs an even more important function: it can run a kind of clinical support to warn of patients whose conditions are deteriorating.

The Neuron EWSS application assigns a numerical score to each vital sign parameter. The total early warning score is then calculated on the basis of the algorithm implemented. The higher the score, the greater the likelihood of deterioration. The score is displayed on the Neuron along with actionable steps for immediate intervention. These might include more monitoring, or even calling the rapid response team right away.

The software algorithm is configured in a secure management tool accessible through a web browser and sent wirelessly to the Neuron at a scheduled time. The management tool is password protected and administered by a trained designee at the hospital, allowing for greater flexibility and complete ownership of the solution.

Naturally, the key to making this simple system effective is to choose the right algorithm for combining vital signs. The United Kingdom is out in front in this area. They developed a variety of algorithms in the late 1990s, whereas US hospitals started doing so only 5 years ago. The US cannot simply adopt the UK algorithms, though, because our care delivery and nursing model is different. Furthermore, each hospital has different patient demographics, priorities, and practices.

On the other hand, according to Niemeier, assigning different algorithms to different patients (young gun-shot victims versus elderly cardiac patients, for instance) would be impractical because mobile Neuron computers are used across the entire hospital facility. If you tune an algorithm for one patient demographic, a nurse might inadvertently use it on a different kind of patient as the computer moves from unit to unit. Better, then, to create a single algorithm that does its best to reflect the average patient. The algorithm should use vital signs and observations that are consistently collected, not vitals that are intermittently measured and documented.

Furthermore, algorithms can be tuned over time. Not only do patient populations evolve, but hospitals can learn from the data they collect. CapsuleTech advises a retrospective chart review of rapid response events prior to selecting an algorithm. What vital signs did the patient have during the eight hours before the urgent event? Retrospectively apply the EWSS to the vital signs to determine the right algorithm and trends in that data to recognize deterioration earlier.

Without help such as the Early Warning Scoring System, rapid response teams have to be called when a clear crisis emerges or when a nurse’s intuition suggests they are needed. Now the nurse can check his intuition against the number generated by the system.

I think clinicians are open to the value of analytics in early warning systems because they dramatically heighten chances for avoiding disaster (and the resulting expense). The successes in early warning systems give us a glimpse of what data can do for more mundane aspects of health care as well. Naturally, effective use of data takes a lot more research: we need to know the best ways to collect the data, what standards allow us to aggregate it, and ultimately what the data can tell us. Advances in this research, along with rich new data sources, can put information at the center of medicine.