OVERVIEW

Measurement is a critical part of testing and implementing changes as well as identifying areas for further research, in prehospital care and dispatch.1 Kaunas, the second largest city in Lithuania, implemented the Medical Priority Dispatch System™ (MPDS®) to improve the efficiency, timely availability, consistency, and reliability of dispatch data and information. This in turn facilitated a research study into the care of cardiac arrest patients.

INTRODUCTION

Measurement is a critical part of testing and implementing changes, as well as identifying areas for further research, in prehospital care and dispatch.2 However, in order to measure a process, a reliable, accurate, and systematic approach is required to collect and process the data. This was just one of the reasons the Kaunas Medical Dispatch Centre in Lithuania implemented the MPDS ProQA™ software (Priority Dispatch Corp, Salt Lake City, Utah, USA).  ProQA, the software platform of the MPDS, is used by Emergency Medical Dispatchers (EMDs) during emergency medical calls for call processing—i.e., to interrogate callers, prioritize the response, and provide Pre-Arrival Instructions. Within the software, each keystroke is recorded in a set of sequences that are stored in a database. The very nature of the design allows for comprehensive data collection, reporting, and measurement.

Prior to the implementation of the MPDS ProQA software, Lithuania used a medical dispatch system inherited from former Soviet Union. Data were collected during emergency calls by writing on special paper-based forms, known as call cards, with the documented times taken from watches prior to the activation of the ambulance response crews.  Whether a patient was category 1 (“urgent”) or category 2 (“non-urgent”) was based on the calltaker’s subjective questioning of a caller, using only personal experience and training to formulate questions, and then referencing a document that listed types of medical conditions and injuries that could be classed as either urgent or non-urgent. This obsolete information-gathering process was time-consuming and created a greater chance of recording untimely, incomplete, inaccurate, and unreliable, mostly non-medical information for the dispatcher and responding crew to use. This resulted in major concerns about quality and data management processes.

The implementation of the ProQA software has allowed accurate, clinical data to be collected; in turn, this has offered the opportunity to conduct research into the call-handing process with a view to improving patient care. This report looks specifically at the range of data collected and how a portion of it has been used to initiate research into the treatment of cardiac arrest patients.

METHODS

Kaunas is the second largest city in Lithuania, with a population of approximately 310,000. On 17th December 2011, Kaunas implemented the MPDS version 12.1 (2010 release). Data on all out-of-hospital cardiac arrest (OHCA) cases were collected for 6 months prior to and 6 months following implementation of the MPDS. Pre-implementation data were collected from paper records; post-implementation data were collected electronically from the ProQA software.  Additional data collected post-implementation (compared to the previous system) included level of consciousness, level of breathing, percentage body area of burns, flu-like symptoms, hazardous chemical details, chemical/biological/radiological or nuclear details, rate and rhythm of breathing, specific stroke symptoms, time between labor contractions, rate of delivery of chest compressions, heart rate, and Pre-Arrival and Post-Dispatch Instructions provided to caller.

RESULTS AND DISCUSSION

The number of data points collected increased considerably after implementation of the ProQA software (Table 1). This has enabled a focus on both operational and clinical performance indicators. Prior to implementation, each emergency call was delineated into eight categories, with two levels of prioritization; after implementation, there are now 1,396 categories across six levels of prioritization (i.e., 17,350% and 200% increases in prioritization levels and categories, respectively).  An improvement has also been realized in the number of core clinical data elements, excluding demographic data, that Lithuania is able to collect—increasing from 1 (almost no information) to 12 (significant and complete clinical information). These increases provide more granular data/information that is more useful in allocation/assignment of more specific response and care. The higher level of data/information granularity also provides more effective support for research.

This increase in the number and accuracy of available data points has enabled the Lithuanian staff to expand research into the critical area of cardiac arrest.  The probability of successful resuscitation of a patient who has suffered an out-of-hospital cardiac arrest (OHCA) increases if all links of the “Chain of Survival” are accomplished.3 We know that efforts to improve survival from OHCA should be aimed at strengthening each link in the Chain of Survival, and one of those links is the provision of early CPR. Thus, OHCA and the dispatcher’s role in the Chain of Survival were appropriate subjects for an initial evaluation of the post-implementation changes.

The MPDS is designed to standardize and code the operation of the EMD, while optimizing safe and effective patient care through PAI instructions. Therefore, each time an EMD identifies a cardiac arrest, he or she is compelled to provide PAIs where possible and appropriate. 6 months of cardiac arrest data before and after the implementation of the MPDS in Lithuania demonstrate the value of this approach, revealing an 18.4% increase in bystander CPR (bCPR) rates, from 44.0% pre-MPDS implementation to 62.4% post-MPDS implementation (Table 2).

There could be several reasons why a portion of OHCA patients (41%) still did not receive PAIs. Some patients may have experienced their OHCA after the termination of the emergency call or once the responding paramedics arrived on scene; the patient may have been obviously dead (e.g., cold and stiff in a warm environment or with decomposition present); the caller may have refused to follow PAIs; and some callers may not have been with the patient, so PAIs would not have been appropriate. Also, EMDs do not always successfully identify a cardiac arrest, and some OHCAs may have been missed at the call-handling stage. When OHCA patients exhibit agonal or abnormal breathing, for example, there is a lower rate of dispatcher-assisted CPR instruction due to misidentification of arrest.

While increasing bCPR is important, return of spontaneous circulation (ROSC) and long term quality of life are the ultimate measures of success.   An overall increase in ROSC was observed post-MPDS implementation in Kaunas. Although not statistically significant, this finding does have clinical importance. Perhaps further investigation into this measure and similar areas, in relation to post-MPDS implementation, will help improve the quality of the instructions provided during bCPR and thus the patients’ ROSC and ultimate quality of life.

CONCLUSION

Regardless of whether an outcome or process metric is used, measurement of data brings new knowledge into practice. Without this measurement, there is no way to tell whether changes are leading to improvement. Measurement is a critical part of testing and implementing changes, as well as identifying areas for further research in prehospital care and dispatch.4 Once the ability to generate and collect accurate data has been achieved, the setting of clear and measureable targets can led to substantial improvements in the delivery of health care.5 The findings in this study have demonstrated that implementation and use of the MPDS, a standardized calltaking process, can significantly improve efficiency, timely availability, consistency, and reliability of dispatch data and information to enable optimal dispatch response.  The implementation of the MPDS has significantly enhanced data collection—for example, in data completeness and increased variety of data types available. High-quality data are now being collected and used in effective decision-making.

It has been determined that the highest priority for research in prehospital care is actually the development of clinical performance measures other than response times,6 and there has been a deliberate move toward the implementation of those clinical performance indicators.7 In addition to providing excellent emergency dispatch services, the MPDS has now enabled scientists in Kaunas to design and conduct definitive scientific dispatch research using specific clinical performance indicators with a view to directly and measurably improving patient care.

TABLES AND GRAPHS


Table 1. Data elements collected before and after MPDS implementation. Y=Data was collected during the period N=Data was not collected during the period


Table 2. Analysis of categorical and continuous measures pre-MPDS and post-MPDS implementation.

REFERENCES

Citation: Barron T, Dobozinskas P, Jasinskas N, Clawson J, Scott G, Patterson B, Olola C. Enhancing emergency medical dispatch to drive specific and significant improvements in patient care. Annals of Emergency Dispatch & Response. 2016;4(1):36-38

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References

  1. Institute of Medicine. Measuring the Quality of Health Care. Washington, DC: The National Academies Press, 1999:2.
  2. Institute of Medicine. Measuring the Quality of Health Care. Washington, DC: The National Academies Press, 1999:2
  3. National Institutes of Health. Emergency Medical Dispatching: Rapid Identification and Treatment of Acute Myocardial Infarction. NIH Publications. 1994; Np. 94.
  4. Measuring the quality of health care; 1999; Molla S. Donaldson, Editor; http://www.nap.edu/catalog.php?record_id=6418.
  5. Commission for Health Improvement (2003) What CHI has found in: acute services. Sector report.  London: The Stationery Office.
  6. Snooks H, Evans A, Wells B, Peconi J, Thomas M. What are the Highest Priorities for Research in Pre-hospital Care? Results of a Review and Delphi Consultation exercise. Journal of Emergency Primary Health Care  2008;6(4).
  7. Siriwardena N, Shaw D, Donohoe R, Black S, Stephenson J. Development and Pilot of Clinical Performance Indicators for English Ambulance Services. Emergency Medical Journal  2010;27:327-31.

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