Sarah Ingleby
Lead nurse, Acute Care Team
Central Manchester University Hospitals NHS Foundation Trust (CMFT), UK
Dr Steve Jones
ICT consultant
Central Manchester University Hospitals NHS Foundation Trust (CMFT), UK
Adverse clinical events are an issue for healthcare organisations and systems everywhere. The incidence, cost and risk factors for in-hospital adverse events are well documented; the Chief Medical Officer for England has estimated that, in terms of hospital operating costs alone, this figure is in excess of £2 billion per year in the NHS.
Despite the suggestion that these events are unpredictable, there is a large body of evidence to suggest that many of them are preceded by documented periods of clinical instability. This instability manifests itself as abnormalities in the physiological observations such as blood pressure or respiratory rate. The recording and documentation of these observations is the start of an event chain that should direct the appropriate clinical response to these problems in order to correct them.
Unfortunately, failure to comply with clinical protocols and, in particular, failure of communication to ensure the most appropriate timely clinical response has been shown to be significant causative factors in the 11% of deaths in England attributable to adverse events.
Our early track-and-trigger system
This was an area of concern for our large metropolitan university teaching hospital, Central Manchester University Hospitals NHS Foundation Trust (CMFT). In 2000, we established a track-and-trigger policy to recognise and respond to acutely deteriorating patients. This was based on the early-warning score (EWS) and was designed to ensure that unstable patients were responded to in a timely fashion with appropriate alleviation of the clinical emergency (Figure 1).
Introduction of the policy saw an improvement in the performance and documentation of observation sets and was associated with an unexpected decrease in the length of hospital stay. Re-education and re-iteration of the policy had maintained the improvement in the performance and documentation of physiological abnormalities. Increasingly, though, our organisation became aware that there were problems with the correct calculation of the track-and-trigger score and especially with the provision of timely clinical responses.
To address this issue, we looked to a technological solution. The incorporation of decision-making algorithms into computer software has demonstrated a beneficial effect in areas such as medication prescribing, weaning from mechanical ventilation, insulin therapy and anti-coagulation. In an attempt to overcome non-attendance, and deliver the most appropriate intervention to individual patients, we co-developed and trialled Patientrack. This is an intelligent alert response system from Australia, designed to track the clinical response and, if inappropriate, unsuccessful or even absent, repeat the alerting process to a series of predefined clinical responses indefinitely until the clinical situation was resolved.
The system in practice
In practical terms, nurses record the patient observations using a wirelessly linked personal digital assistant (PDA). This is done at the bedside in real time and these observations are used to correctly calculate the track-and-trigger score – solving one of our concerns. The level of this score dictates the frequency and timing of the next set observations. A target time is published on a viewing screen so that staff are aware when the next set of observations are due. Any delay in the performance of these observations is also published on the viewing screen and, in the event of a prolonged delay, a bleep is raised to the ward manager.
Our next concern was that of the provision of a timely clinical response. We were aware that there was a communication gap between nursing staff and the clinical responders. In a similar way that the nurse managers are informed of a delay in observation taking via a bleep alert, the clinical responders, in many cases doctors, are bleeped.
This bleep was received by a different seniority of doctor, with a different response time according to the degree of abnormality of the track-and-trigger score. The bleep contains information including the patient name, ward, track-and-trigger score and response time. Abnormal scores are also published on the viewing screen so that staff are aware that a particular clinical responder has been alerted and when they are due to arrive.
All of this is controlled by the Patientrack software, which runs on a central web server with an underlying data repository. A modifiable set of configurable ‘business rules’ determine how the system reacts to the observations provided. In our case, the rules were based on the CMFT track-and-trigger policy (Figure 1).
The trial
Our trial of this system was historically controlled and focused on two of our medical wards: the medical assessment unit (MAU) and an acute medical ward. This replicated the normal pathway of non-specialty medical patients through the hospital (i.e. Emergency Department to MAU to general medical ward). The MAU admits approximately 600 patients per month.
Our implementation was in three phases: baseline data collection, the implementation of the PDAs and, finally, the addition of automatic alerting in response to abnormal EWS scores. The baseline data collection took place over 47 consecutive days between November and December 2007; there were 705 patients generating 7,820 observations. The alert phase data collection took place over 38 consecutive days between August and September 2008; there were 776 patients generating 5,848 observations. No patients removed themselves from the study.
Because the system drove observation recording, we were able to improve compliance with our policy. Based on these observations, miscalculations of the track-and-trigger scores were stopped.
The documentation of a clinical response to a patient with an EWS of 3, 4 or 5 increased from 29% at baseline to 78% in the alert phase (p<0.0001). For those patients with a score of 6 or more, a clinical response was recorded in 67% of instances during the baseline phase and in 96% of instances during the alert phase (p<0.0001). The timeliness of these responses was difficult to measure in the baseline group because of poor documentation. In contrast, the electronic system automatically documented both the time of EWS data entry and the times of clinical responses for the patient.
The number of patients admitted to critical care and corresponding length of stay there during the baseline and intervention periods were 14 patients (51 bed days) and five patients (26 bed days) – (p=0.04) – respectively. Our primary outcome measure was hospital length of stay and this, too, was significantly reduced in the alert phase (9.7 days vs 6.9 days, p <0.001).
Moving forward
Following the trial, the CMFT board believed this was a system that would greatly benefit our patients and agreed to support an organisation-wide implementation. The assurance that observations were completed in full, accurately and in a timely manner as well as driving appropriate and effective responses, matched our organisational patient safety aspirations.
This was supported by evidence from another CMFT patient safety initiative that demonstrated that 20% of cardiac arrest alerts were related to ineffective compliance with the track-and-trigger policy. A clinical project team – the Acute Care team – was assembled to conduct the implementation. Its focus was two-fold: roll out of the Patientrack system across the organisation and the educational initiatives associated with the acutely deteriorating patient.
The clinical competencies and standards required to safely manage this group of patients were set out in the clinical guideline CD50 from the National Institute of Clinical Excellence (NICE) entitled The Acutely Ill Hospital Patient (2007). From the outset, we set out to ensure that the skills required were available at all times within the organisation. Standards for observation-taking and acute care skills were established and training undertaken as required to ensure that these skills were held by the relevant clinicians to recognise and respond to deteriorating patients.
There are an increasing number of wards live on the system and the benefits are becoming more appreciated. The ability to highlight, monitor and adjust the timings of observations has been immediately beneficial to patients. The nursing ward managers are now able to support individual nurses as soon as observations highlight a deteriorating patient; this has been an excellent learning tool. Nursing staff report that the reduction in time spent trying to make doctors aware of the acutely deteriorating patient enables them to spend more time with the patients.
Handover reports are generated that are used to highlight patients that have triggered over the previous 24-hours to guide and assist with the recognition of patients who require a priority review. Other reports can also be generated for use by clinical teams to identify the timeliness of response which can help identify periods of higher workload; already the alerts to one group of clinicians was reviewed and adaptations made to the alerting process.
With the implementation of the acute care initiatives and the use of the system, there have been clear advantages to patients. As expected, the process of change for staff has made the process an interesting and challenging one, but the benefits to patients are proven.
