examining repeat ems incidents and community paramedicine programs
TRANSCRIPT
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Emergency Sign Image courtesy of www.healthimaging.com Ambulance Image courtesy of VBEMS Fall Prevention Image courtesy of livhomeblog.com
2014
Robert M. Davis, MPA
Public Safety Analyst
6/1/2014
The City of Virginia Beach, Department of Emergency Medical Services: Examining Repeat EMS Incidents and Community Paramedicine Programs
The purpose of this study is to examine the City of Virginia Beach’s department of
emergency medical services and to identify (if existing) repeat incidents and patients
that would qualify as “frequent flier” events. To identify demographic
characteristics based on age for examination of a relationship (if existing)
between selected variables.
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The City of Virginia Beach Emergency Medical Services: Examining Repeat EMS Incidents and Community Paramedicine Programs
Introduction
“Repeat EMS callers costs taxpayers millions2”, “Trivial calls to 911 costly to taxpayers, investigation finds3”, “Chronic EMS users costing Kansas City taxpayers millions4” are examples of some of the headlines that are being reported by variety of news outlets and information network sources. These statements are not made entirely without validation as repeated calls for emergency medical services can be a drain on EMS systems financially and operationally5 6.
In 2009, the media organization CNN ran a story in which the then president of the National Association of Emergency Medical Technicians (NAEMT) Jerry Johnston discussed the billions of dollars7 wasted as a result of EMS system abuse.
The National Fire Protection Association, which tracks 911 call volume annually, said fire departments nationwide responded to about 15.7 million total medical aid calls in 2008. Using that data, the National Academies of Emergency Dispatch, said about 20% of the calls are classified as non-life-threatening and don't require a paramedic8.
Individuals whom call for EMS service and may not require immediate care or those individuals who are seen more than once in given year by EMS responders are referred to as “frequent fliers”9; either as a result of abuse on the system (e.g. calling for an emergency when there is none) or necessity (e.g. chronic health problems), frequent fliers can have a detrimental impact on the service delivery of EMS services.
The detrimental effect being financial cost on local governments and private EMS providers which are obligated to respond to calls made for 911 emergency medical services regardless if the individual in need may require actual emergency medical care. Costs may translate into additional staff, equipment and man hours dedicated to providing either non-emergency treatment or non-transport responses to 911 calls made at the patients request, while actual calls for emergency medical care may be delayed (e.g. tying up ambulances and medics limiting response capability) to those patients in need.
2 https://www.youtube.com/watch?v=pNfBYG_SKFo (NBC Action News, 2014)
3 http://www.springfieldnewssun.com/news/news/local/trivial-calls-to-911-costly-to-taxpayers-investi-1/nMskk/
(Sweigert, 2011) 4 http://fox4kc.com/2014/05/20/chronic-EMS-users-costing-kc-taxpayers-millions-says-fire-chief/ (Banks, 2014)
5 Contextually speaking
6 (Science Daily, 2011)
7 Nationally (Kavilanz, 2009)
8 (Kavilanz, 2009)
9 (Sweigert, 2011)
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While the City of Virginia Beach does not bill or remit some form of payment for services provided, there are still costs associated with maintaining the operational capacity of EMS service delivery:
Paramedics
Medical providers
Equipment and supplies (medical and transport)
Maintenance of resources
These are all components of EMS service delivery in which costs may be incurred. The individual non-profit rescue squads which operate and provide the majority share of EMS services (e.g. volunteer medics, staff, support personnel etc.) incur costs in providing funding to maintain, recruit and train volunteer medical personnel.
An intangible form of costs which may be difficult to quantify or provide a validated measure is “health and wellness of the community”. This value is interrelated with the mission the department of emergency medical services adheres by
The mission of the Department of Emergency Medical Services is to deliver valued services to the community that preserve life, improve health, and promote the safety of citizens and visitors, who live, learn, work and play in our community while maintaining a sustainable systems approach that is focused on dynamic resource utilization to enhance the overall quality of life in Virginia Beach10
Preserving life, improving health and promoting safety are additional forms of costs; costs to life and health and wellness of the community. An individual whom may be identified as a repeat patient or frequent flier is incurring a costs, a costs to their own health and the community as a whole may in turn be affected by this costs. If an ambulance responds to a call for 911 for a repeat patient with a chronic health condition, then at the same time a call is made for a cardiac arrest the ambulance (hypothetically) can only respond to one call at a time. As a result, emergency medical care may be delayed and the chronic patient may not best be attended by emergency medical care but rather by a medical care provider (e.g. primary care physician).
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(Emergency Medical Services: Our Mission, 2014)
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Contents Executive Summary ................................................................................................................................... 5
Objective ...................................................................................................................................................... 6
Methods ....................................................................................................................................................... 6
Data Limitations .......................................................................................................................................... 7
Findings ....................................................................................................................................................... 8
How many repeat incidents have occurred? ...................................................................................... 8
Repeat Incidents and Patient Age ..................................................................................................... 10
Weakness of small sample size ......................................................................................................... 17
Concluding the statistical test ............................................................................................................. 17
Nature of repeat incidents ................................................................................................................... 17
Concluding findings .............................................................................................................................. 21
Further variable testing ........................................................................................................................ 22
How have other EMS agencies tackled repeat incidents? ............................................................ 22
Wake County North Carolina .............................................................................................................. 23
Fort Worth Texas .................................................................................................................................. 23
Eagle County Colorado ....................................................................................................................... 24
Concluding EMS agencies .................................................................................................................. 24
The future issues of repeat incidents and readmissions ................................................................ 25
Conclusion ................................................................................................................................................. 26
Analyst’ Recommendations .................................................................................................................... 27
Works Cited ................................................................................................................................................. 29
Appendix .................................................................................................................................................... 31
Appendix A ............................................................................................................................................ 31
HIPAA................................................................................................................................................. 31
Appendix B ............................................................................................................................................ 32
Where did the data come from? ..................................................................................................... 32
Addendum ................................................................................................................................................. 33
GIS Spatial Mapping ............................................................................................................................ 33
Where to target outreach? .............................................................................................................. 33
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Figures
Figure 1: # of observed incidents; July 2010-May 2014 (Imagetrend Dataset) ................................ 8
Figure 2: # of observed repeat incidents; July 2010-May 2014 .......................................................... 9
Figure 3: % share of incidents repeat and non-repeat; Year 2011-Year 2013 ................................. 9
Figure 4: % change in incidents/repeat; Year 2011-2012 and Year 2012-2013 ............................ 10
Figure 5: # of repeat incidents by repeat patient age groups; Year July 2010-May 2014 ............ 11
Figure 6: % share of population and % share of repeat incidents; Year July 2010-April 2014 .... 11
Figure 7: % share of repeat incidents by % share of population age ............................................... 12
Figure 8: % share of repeat incidents patient age groups ................................................................. 12
Figure 9: % share of repeat incidents; repeat patient age groups .................................................... 13
Figure 10: Repeat patient regression analysis .................................................................................... 14
Figure 11: # repeat patients patient age group .................................................................................... 15
Figure 12: Repeat patient linear regression ......................................................................................... 15
Figure 13: Regression statistics ............................................................................................................. 16
Figure 14: 2010-2014 dispatch complaint repeat incidents ....................................................................... 18
Figure 15: % share of repeat incident patient disposition; Year July 2010-May 2014 ............................... 19
Figure 16: ALS&BLS transports repeat incidents; year 2011-2013 ............................................................. 20
Figure 17: Unit hours dedicated to repeat incidents per month; year 2011-2013 .................................... 21
Figure 18: Fall density per square mile (left) and population 60 and over by census tract (right) ............ 34
Figure 19: Nursing home locations pin mapped and nursing homes overlaid with fall density per square
mile ............................................................................................................................................................. 35
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Executive Summary
The number of EMS calls dedicated to repeat incidents and repeat patients has continued to grow during the observable years in which data was available (July 2010-May 2014).
ALS and BLS transports comprise the majority share of EMS service dedicated in response to repeat incidents and repeat patients.
Transports for both ALS and BLS patient disposition can place an ambulance out of service for 01:36:00 on average according to data compiled from annual year 2013.
Repeat incidents incur a form of costs in both unit hours lost and personnel required to respond to repeat incidents.
EMS agencies across the nation have implemented repeat incident, repeat patient intervention programs to reduce the occurrence of these events.
Hospitals will be financially punished by repeat incidents and patients under the Affordable Care Act.
EMS and hospitals exist in a shared relationship when examining community health, safety and well-being.
EMS data is limited to evaluate causal relationship of why repeat incidents are happening.
Hospitals have financial incentive to reduce repeat incidents; EMS has a moral/ethical public service obligation in reducing repeat incidents and repeat patients.
Working with hospitals to collect patient outcome data and demographic data may be beneficial to understanding repeat incident and repeat incident issue in City of Virginia Beach.
Opportunity to improve efficiency and allocation of existing resources; reduce operating costs while improving the health and well-being of the community.
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Objective
Existing research and studies examining EMS service delivery has identified a national trend in the occurrence of “frequent fliers”, or individuals whom request EMS services more than once in a given annual year. The purpose of this study is to examine the City of Virginia Beach’s department of emergency medical services and to identify (if existing) repeat incidents and patients that would qualify as “frequent flier” events. To identify demographic characteristics based on age for examination of a relationship (if existing) between selected variables. To examine if a “cost”11 is associated with responding to repeat incidents.
Methods
A population based retrospective study was implemented for this analysis. Data for this study was obtained from the Department of EMS’s emergency medical recording data system Imagetrend. All patients12 with a reported incident were included in the sample size extracted for analysis in the study. The time period examined was from July 2010 to April 201413. Designations of repeat patients were determined based on the number of observable incidents reported during the time period selected. Patients were sorted on the bases of first and last name, date of birth and number of reported incidents during each annual year period14; those patients whose name and date of birth contained multiple incident observations in a given year were designated as a repeat patient and added to a database to be used in calculating derived statistics. Patient age, location of incident, reported primary impression were additional data values collected in this analysis.
Repeat incidents were determined by patient name, date of birth and dispatch complaint; if an observation had more than one observable incident occurrence in the year examined15, then it was designated as a repeat incident16.
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Unit hours dedicated to the incident 12
All patients with completed patient record information. Records with partial names, no date of birth or other incomplete values used in the methodology to determine repeat patients and incidents were discarded from being included in the database used to calculate statistics and measures. Currently there exists a lack of a mechanism to determine if incomplete patient reports can be validated as “actual” incidents. Analyst’s determined to exclude incomplete records as a means to limit invalid data observations. As a result, total reported incidents in this analysis may not be comparable to other existing/prior/future reports examining the City of Virginia Beach’s incident observations 13
The available time span by which data was available to be collected and disseminated 14
July 2010-December 2010, 2011, 2012, 2013 and January 2014-April 2014 15
12 month period 16
Same patient requesting the same EMS service response more than once in a 12 month period. Example: John, Doe-05/13/1971-Fall; fall incident observed May, 05 2010 and fall incident observed July, 17 2010
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Following identification of repeat patients/incidents, statistics were generated from among those observations to prepare data analysis and employ statistical testing models to examine the demographic variable of age and the number of repeat incidents to determine if a possible correlated relationship was/is present.
The final portion of this analysis examines case studies of existing EMS service providers from across the nation to examine employed programs or methods with how repeat patients are being addressed.
Data Limitations
Upon analysis of the data collected, it was determined that there exist reporting errors. The depth or degree of error is currently under review, but at the time of this analysis no determination of “degree error” may be assessed. Types of reporting errors observed:
Misspelling of patients name
Misspelling of patient address
Incorrect format entry for patient date of birth
Incorrect format entry for incident number
Blank values in value fields
While these errors may seem “negligible” at first glance, they impact the ability to provide accurate and valid measures which are used in analysis such as this report. This hinders the ability of the analyst’s to provide validated data and statistics in relation to EMS service delivery. While a degree of error may be acceptable, it should not become the norm in which complacency may take root; the fear being that future analysis will only become encumbered with an even greater degree of observable errors. This analysis represents the best efforts of the analyst’s to collect validated and accurate data which was used in the work of this report. However, please be cognizant of the fact that the findings of this analysis may not be deemed 100% reflective of actual occurrences in the field. Do to data error and miss-reporting, only values by which validation could be provided were accepted. Refer to the methods section for a review of how values were validated for use.
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Findings
How many repeat incidents have occurred?
Examination of the data pertaining to repeat incidents of EMS calls for service reveals that over the 47 months in which data was available, there have collectively been 13,051 repeat incident occurrences. The total number of reported incidents17 for the same time period observed was 125,119. The data illustrates that during the periods of observation, 10% of the total incidents which occurred were comprised of repeat incidents. Analyzing the number of repeat incidents in comparison to the number of non-repeat incidents, repeat incidents have continued along a positive linear trend of growth and have comprised a larger share of the total number of incidents reported.
Figure 1: # of observed incidents; July 2010-May 2014 (Imagetrend Dataset)
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Incidents observations calculated only reflect those as outlined in the methodology requirements. Actual number of reported incidents is higher than what is presented in this analysis. Observations that did not match the value requirements set for this analysis were excluded from calculations employed in this analysis.
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Figure 2: # of observed repeat incidents; July 2010-May 2014
Figure 3: % share of incidents repeat and non-repeat; Year 2011-Year 2013
10% 11% 12%
90% 89% 88%
0%
20%
40%
60%
80%
100%
2011 2012 2013
% Share of Incidents/Repeat
% Share of Incidents (Repeat) % Share of Incidents (Not Repeat)
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Figure 4: % change in incidents/repeat; Year 2011-2012 and Year 2012-2013
Three years of data observations limits the ability to draw any long term conclusions or provide a significant historical analysis to identify any observable trends or patterns from the reported data. However, the data that is available provides that the number and occurrence of repeat incidents has been on the rise in terms of occurrence.
Repeat Incidents and Patient Age
During the analysis, patient age was one of the variables examined among the observations of repeat incidents and repeat patients. Upon examination of those variables, it was found that there may be a correlated occurrence between the number of reported incidents and the age of the patient in relation to that incident. The older the patient, the higher the occurrence of incidents being reported as “repeat incidents”; in summation, age may be an indicator of probabilistic determination of the occurrence of a repeat incident.
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Figure 5: # of repeat incidents by repeat patient age groups; Year July 2010-May 2014
Patients aged 60 and older comprised the highest rate of repeat incident occurrences. These age groupings were based off of the age groupings used in the U.S. Census Bureau’s American Community 1 Year Survey Estimates; this was done to create a comparable comparison to reported population estimates for the City of Virginia Beach.
Figure 6: % share of population and % share of repeat incidents; Year July 2010-April 2014
Figure 6 illustrates the % share of the population by defined age groupings (green bar); while the red line illustrates the % share of that same population group of repeat incidents occurrences. While the age groups 60 and older comprise the smallest share
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of the total population, they comprise the largest % share of repeat incident occurrences during the time period observed.
Figure 7: % share of repeat incidents by % share of population age
By breaking down the age groups into two simplified comparable groupings; one group representing the population age 60 and younger and one group representing the population 60 and older, the differences in % shares of repeat incident occurrences is even a more stark comparison of observation.
Figure 8: % share of repeat incidents patient age groups
Just examining the number of repeat incident occurrences, the same observation can be identified in conjunction with the other findings from the data that patients age 60 and older comprise the majority share of repeat incident occurrences. Given the results of
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the analysis and the prevalence of the data findings, an observation can be hypothesized:
The older the age of the patient, the greater the likelihood that that patient will be involved in a repeat incident. Older patients will contact and request EMS services more than once per annual year at a rate more frequent than those patients of a younger age. The null hypothesis being that patient age has no correlated relationship with the probabilistic occurrence of repeat incidents.
Figure 9: % share of repeat incidents; repeat patient age groups
Examining the broader age group categories once again, those patients which are associated with an older age group show a higher % share of incidents being repeat incidents. That as the group becomes older, the number of repeat incident observations increase in kind.
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Figure 10: Repeat patient regression analysis
In order to determine if a correlation is present between patient age and the occurrence of repeat incidents, a regression analysis was performed utilizing the dataset developed from the pulled data for repeat incidents. This comprises observations made from year July 2010-May 2014. In total, 104 observations were made utilizing two variables: Repeat Patient Age and Number of Repeat incidents from a dataset comprising 13,919 individual observations. These observations were then categorized by patient age to develop the 104 observations used in the regression analysis.
The regression analysis shows that as the age of the patient increases, the number of reported repeat incidents increase. This is a positive linear trend (e.g. the overall data represents an increase in observations as patient age increases). The number of repeat incidents decreases continually from patient age 88 to patient age 104. This decline in the occurrence of repeat incidents is not surprising given that the population age 85 and older comprises the smallest % share of the total population at 1.3%18 ; also taking into consideration that the average life expectancy for Virginia residents is 78.5 years of age19. This decrease in the number of occurrences can be explained simply as a result of a decrease in the sheer number of patients that are included within the age group of 88 and older. The age group of 75-87 represents the largest group of which repeat patients are comprised.
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5,581/442,967=1.3% 19
(Virginia Performs, 2014) Male average life expectancy=76.3, Female average life expectancy=80.7
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Figure 11: # repeat patients patient age group
The hypothesis stated that a correlation between patient age and the occurrence of repeat incidents would be present among the data observations. The null hypothesis states that there would be no correlation between the variables of age and repeat incident occurrences from the data. To either accept or reject the null hypothesis, a linear regression statistical test was used to test for the correlation coefficient; in other words, the effect of the degree of change in one variable on the other.
Figure 12: Repeat patient linear regression
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Figure 13: Regression statistics
In order to test the correlation between patient age and repeat patient incidents, the age of patients were collected into four group categories:
Group 1 Group 2 Group 3 Group 4
Age 1 to 18 Age 19-39 Age 40-59 Age 60 to 100+
Due to the non-linearity of the raw data values and non-normal distribution of those values, categorization of the values into groups was used to normally distribute the data observations collected form the data. Using the raw data values would have allowed for a greater degree of data observations to be used in calculating the regression statistics, but non-normal distributed data does not work in the regression model for statistics testing; especially when determining the correlation coefficient. The grouping of the raw data into categories result is similar to the results already presented in this analysis. As patient age increases, the number of repeat incident occurrences increase as well.
The linear regression analysis performed produced a correlation coefficient of 0.97 which signals a strong positive linear relationship. This means, that as the value of the variable X (e.g. patient age) increases, there is an observable change in the variable of Y (e.g. number of repeat incident occurrences). Correlation does not prove that patient age is causing the change seen in number of repeat incidents, but rather the degree of linear association between the variables.
R square is another statistic that aids in determining the strength of correlation between patient age and repeat incidents. R square=0.95; this means that 95% of the variance in the number of repeat incidents may be explained by the linear relationship between patient age and the number of repeat incidents. This measure is the distance of each of the points from the linear line, closer the points are to the line, the stronger the correlation.
The final statistic to examine is Significance F; this statistic measures the regressions’ output as being generated as a result of chance, the smaller the value the less probability that the results were generated by mere chance. The Significance F=0.02, means that there was only a 2% probability that the regression results could have occurred due to chance. This value indicates the results are statistically significant.
Regression Statistics
Multiple R 0.976369313
R Square 0.953297035
Adjusted R Square 0.929945553
Standard Error 717.6790021
Observations 4
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Weakness of small sample size
It should be kept in mind that this regression analysis utilizes a small number of observations. The weakness of this being that it may not accurately portray the likelihood of a correlation existing between the two variables used in the model. The problem being that any change in the variables or an outlier value may significantly alter the results of the regression analysis. There is much debate about the validity of small sample size results in statistical testing, it is a subjective debate between academics and professionals and whether or not the results should be taken as valid or invalid will come down to the individuals preference. This analysis takes no particular side in regard to that debate, instead it provides the results of the statistical test and makes acknowledgement of its findings and how they may be interpreted.
Concluding the statistical test
After conducting a linear regression to examine the correlation between the variables of patient age and number of repeat incidents, the findings support rejection of the null hypothesis that there is no correlation between patient age and number of repeat incident occurrences. The findings instead support the conclusion that there is a strong positive linear relationship between the variables of patient age and repeat incidents. As the age of the patient increases, the probabilistic likelihood that the number of repeat incidents associated with that age will increase in kind; it is statistically significant that a change in patient age will result in a change in number of repeat incidents not by random chance.
Nature of repeat incidents
The data analysis illustrates that there is an increase in the amount of repeat incidents occurring and that older patient’s age 60 and older make up an increasingly larger share of those occurrences. The next portion of this analysis will examine what those incidents are comprised of thorough examination of dispatch complaint and the type of EMS service provided to these repeat incidents. The nature of service provided can be an indicator to help evaluate the type of resource needed in responding to these events and subsequently the cost dedicated to them.
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Figure 14: 2010-2014 dispatch complaint repeat incidents
The data observations collected illustrate the top dispatch complaints which comprise 5% or more of the demand for EMS services reported from July 2010-May 2014 for repeat incidents. Analysis of each individual year by which a full calendar year of data was available20 holds that the reason for dispatch has remain unchanged for the period observed among repeat incidents.
Breathing Difficulty
Chest Pain
Fall Victim
Sick Person
Seizure Convulsions
Diabetic Problem
Abdominal Pain
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Year 2011-2013
Dispatch Complaint Number of Repeat Observations % Share
Breathing Difficulty 3,480 25.00%
Chest Pain 1,989 14.29%
Fall Victim 1,750 12.57%
Sick Person 1,704 12.24%
Seizure / Convulsions 1,030 7.40%
Diabetic Problem 953 6.85%
Abdominal Pain 804 5.78%
2010-2014
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These are the top 5% dispatches that are comprised of repeat incidents during years 2011-2013.
Disposition of repeat incidents
Figure 15: % share of repeat incident patient disposition; Year July 2010-May 201421
Data findings illustrate that transport based services comprise the majority share of services provided in response to association with repeat incidents. Among types of transport services provided, BLS22 designation service makes up the majority share. Examining patient disposition designation is beneficial since it classifies the type and extent of resources that may have been needed. ALS designated responses require emergency medical providers that are State certified at a level of training and experience higher than that of BLS emergency medical providers. Areas of costs in examining BLS and ALS designations would be training costs, materials and certification encumbrances that would be affiliated with each of the emergency medical provider designations.
Transport services both ALS and BLS come at a time costs known as “unit hours”. Unit hours are essentially the equivalent of the amount of hours an EMS response unit is available for service in a given 24 hour period.
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Other category is a grouping of the following dispositions: mutual aid only, pronounced dead at scene, stand by only, treated transferred care, treated turned over to police, treated transported by pov, blank. 22
Basic Life Support
20
Example:
You schedule 12 ambulances for one 24 hour day. There are 288 available unit hours available for that day. 24 hours per Unit; 12X24=288; therefore, if the number of hours spent out of service in a 24 hour period are greater than 288 hours, then this signals a delay for EMS response. If a unit is on call for an emergency, it cannot simply leave the current patient and travel to the next one. In essence, time itself is derived as another form of costs.
During year 2013, the Department of EMS conducted a study examining Unit out of service times during the year; out of service time is the time a unit spends on call and is out of service or unable to respond if a new call for EMS is requested. The 2013 analysis found that 90% of unit out of service times for ALS and BLS transports were 01:36:00 (One hour and 36 minutes). Examining that the % share of repeat incidents are comprised of transport ALS and BLS designations, roughly 295,46423 unit hours would have been dedicated to repeat incidents over the course of years July 2010-May 2014.
Figure 16: ALS&BLS transports repeat incidents; year 2011-2013
Knowing that ALS and BLS transports comprise the majority share of EMS responses, and that transports both ALS and BLS have inherent cost associated with them, the data was examined to see if transports have seen any changes over the periods of observation. As figure 16 illustrates, the number of both ALS and BLS transports pertaining to repeat incidents has seen a positive linear increase over the three full calendar years. This observation would seem to correlate (observationally) along with the increase in repeat incidents that have been observed and illustrated in this analysis.
23
6,762 ALS transports + 5,549 BLS transports * 24 = 295,464 hours
21
Figure 17: Unit hours dedicated to repeat incidents per month; year 2011-2013
Operating under the 90% of ALS and BLS transports requiring 01:36:00 of out of service time, a rough estimation of the number of unit hours dedicated to repeat incidents per month for each of the observable years was calculated24. As the number of ALS and BLS transports have increased, so too has the number of unit hours dedicated to responding to repeat incidents increased as well. These unit hours should be taken in conjunction with the unit hours dedicated to non-repeat incidents as well as the data analysis shows that non-repeat incidents resulting in ALS and BLS transports have increased during the observational period as well.
Concluding findings
The findings of this analysis illustrate that the occurrence of repeat emergency medical incidents is on the rise. More calls for EMS service are being dedicated to repeat patients for the same nature of emergency medical need. There is a strong positive correlation between patient age and the number of repeat incident occurrences; the older the patients age, the increased likelihood they will request EMS services for the same medical issue more than once in a given annual year.
Time dedicated to a repeat incident comes at costs to the Department of EMS in relation to unit hours and personnel. The number of unit hours dedicated to repeat incidents has increased along with the observation in the increase of repeat incidents. The majority share of repeat incidents result in either ALS or BLS designated transport services being provided which require a greater amount of time and personnel skill/training than other care designations. In turn, this requires more resources and
24
Total unit hours/(24 hours in a day*30 days in a month)=Total unit hours per month
22
materials being provided to incidents which are of a repeat nature in which emergency medical care may not be the best treatment for the patient in question.
Patients who are diagnosed with a chronic condition (e.g. diabetes), elderly patients taking multiple medications, mental health patients and those patients who may request emergency medical care but are never admitted to the hospital due to lack of “illness” or “injury”. As a result, this may increase the likelihood that simply providing an EMS transport or care at the scene of the incident will do little in curbing the likelihood that a patient with a chronic, recurring illness or –malady will require similar need for EMS service in the given year. Unfortunately, there are limitations as to the breadth and depth of analysis that can be conducted internally for Virginia Beach EMS with the given reported data and the capability of the reporting software (imagetrend) at this current time.
Further variable testing
The department of emergency medical services is currently in negotiations with Sentara Hospitals (e.g. regional supplier of medical services and hospitals in the Virginia Beach area) to secure patient data of those patients which are transported to Sentara affiliated hospitals. Currently, EMS data can only be reported and collected during the time the patient is in the care of EMS (e.g. being provided care or transport to designated hospital provider). As a result, what happens to the patient once they are dropped off is beyond the grasp and ability of EMS to record or collect data.
This limits the amount of variables which can be examined between emergency medical care and patient care. Was the patient admitted? What was their primary diagnosis? Was their need to be transported valid? What effect did EMS procedures/administrations have on the patients’ health prior to being admitted to the hospital? There are a bevy of questions and analysis that could be conducted if there was simply more data that could be collected and analyzed. Responding to the call for EMS service and dropping the patient off at the hospital is only half the story of what happens to a patient.
How have other EMS agencies tackled repeat incidents?
Given the limitations of analysis that can be performed from a clinical component, capturing patient outcome data and other demographic values for analysis, there may still be benefit in exploring other EMS agencies across the country and seeing how they have approached the issue relating to EMS repeat incidents within their own respective localities. This is a case study approach by which individual agencies were surveyed to collect information pertaining to procedures, policies or actions that have been taken to intervene in relation to repeat EMS incidents.
23
Wake County North Carolina
Wake County, North Carolina EMS instituted an Advanced Practice Paramedic (APP) program in year 2009. The program was aimed to improve the efficient allocation of EMS resources through the reduction of frequent and repeat incidents for EMS services. This was accomplished by screening patients through medically trained paramedics than the traditional ambulance response (e.g. assess patient and transport to the ambulance). On site evaluation by the paramedics were then used to determine the proper response for the given EMS incidents which were more applicable to the situation and medical need at hand25.
The APP program works in tandem with medical providers, social services which are under the umbrella of a regional partnership in the area known as the Capital Area Collaborative (CAC). Together, the paramedics of Wake County EMS and workers with the CAC identify “at risk” patients that are identified under the repeat caller list and notification of a Medicaid recipient being discharged from the hospital. These responses are follow-ups to these patients in which medical checkups are performed and questions are answered in regard to the medications being taken by the patient. The goal being that a problem can be prevented before it occurs26.
The cost savings which have been realized for the instituted use of this program, may translate to a total savings of 1 unit hour and $2,000 in EMS transport cost per patient through proper referral and diagnosis of an EMS patient before transporting them to an Emergency Department where the patient may be simply discharged and not admitted27.
Fort Worth Texas
EMS provider Medstar instituted a Community Health Program in 2009 which identified repeat patients that were designated as “high users”; those patients who would request EMS services more than 15 times in a given annual year. These patients were then given individualized care plans by which Paramedics would conduct a series of home visits where basic tests and assessment are performed, medications the patient is taking reviewed and family members and the patient are educated about care management. This information is then coordinated with the patient’s case manager or primary care physician to help arrange follow ups with the patient if needed. The goal of the program is to reduce need for readmission to an emergency department by intervention of education and refereed care to designated medical providers28.
25
(EMS, n.a.) 26
(Kirkwood, 2009). 27
See footnote 26 28
(Medstar, 2014)
24
From January 2010 to April of 2014 the Medstar Community Health program realized cost savings of $4,189,788 in EMS and emergency department charges; this comes to $47,611 per patient savings realized as a result of the programs’ execution29.
Eagle County Colorado
Eagle County Paramedic Services developed a Community Paramedic program which involves house calls by paramedics to patients in need of non-emergency medical services. This service is provided to both insured and uninsured individuals living in the area and patients are referred to the program by their doctor. The paramedic is able to perform a variety of medical assessments for the patient and pass along those assessments and tests results to their doctor of physician to help determine need for medical intervention before an emergency medical situation occurs30.
Concluding EMS agencies
These are only a few of the programs that have been implemented across the nation to combat the issue of repeat incidents in EMS. The goals of each of the programs examined at their core are similar, in that they seek to reduce the occurrence of repeat incidents and the patients that are the drivers of those incidents through intervention based programs to monitor, educate and prevent the occurrence of emergency medical situations and reduce the number of 911 calls for non-EMS situations. Two of the programs examined realized cost savings in the form of unit hours saved and the amount of medical charges that were prevented from being charged had the patient been traditionally picked up and transported to an emergency department.
Hurdles in Virginia to provide community paramedicine
Under existing Virginia Emergency Medical Services Regulations, the ability for EMS agencies operating in Virginia may be hindered from developing or enacting the execution of a community paramedicine program like the ones carried out by Fort Worth Texas, Wake County North Carolina and Eagle County Colorado. Regulations 12VAC5-3131 stipulate that many of the services provided to in-home care of patients in non-emergent situations do not fall under the language of those services granted and performed by Emergency medical Services agencies; rather, such programs are typically provided by home care organizations as outlined under 12VAC5-381-239 through 12VAC5-381-36032.
The Office of Emergency Medical Services under the authority of the Virginia Department of Health advises that EMS agencies interested in pursuing or providing
29
(Expenditure Savings Analysis, 2014) 30
(CBS 4 Denver, 2011) 31
(Virginia, CHAPTER 31 VIRGINIA EMERGENCY MEDICAL SERVICES REGULATIONS , 2014) 32
(MOBILE INTEGRATED HEALTHCARE/COMMUNITY PARAMEDICINE LICENSURE REQUIREMENTS, 2014)
25
community paramedicine based intervention will need to be licensed as home care organizations by the Virginia Department of Health, Office of Licensure and Certification, in order to comply with existing Virginia law.
The future issues of repeat incidents and readmissions
With the passage of the Affordable Care Act, one of its provisions33 stipulates financial penalties to hospitals which have too many readmissions34; this financial punishment can come in the form of a maximum 1% reduction in Medicare’s regular payments for every patient over the next year35. The punishment for recurring readmissions of repeat patients for repeat incidents is to assert pressure on hospitals to pay more attention to what happens to the patient following their discharge, as well as to slow the growth of Medicare’s spending which is projected to grow annually by 4%36.
In addition to repeat admissions of repeat patients, there is also the satisfaction reported by these patients that will come with a financial stipulation that can be levied against hospitals37. Under the Affordable Care Act, patient survey results will be used to determine Medicare bonuses or punishments based on the results of the survey filled by patients. The patient response component will make up as much as 30% of the value used in the calculation model to determine Medicare re-imbursement bonus or punishment to the admitting hospital38.
A repeat patient that is a beneficiary of Medicaid, whom is transported by EMS to an emergency department more than once in a 30 day period for the same medical incident, may result in a financial penalty being leveraged against the admitting hospital. If a repeat patient is admitted to the hospital for a non-emergency medical service (e.g. toothache) and waits to be seen by a doctor whom determines the patient transported is in no need of medical care and is discharged, that patient may file survey responses that negatively evaluate the care provided by the hospital which in turn may result in a financial penalty being levied against the hospital. There are economic incentives that will now be in play in regard to repeat patients and repeat incidents.
33
Section 3025 of the Affordable Care Act added section 1886(q) to the Social Security Act establishing the Hospital Readmissions Reduction Program, which requires CMS to reduce payments to IPPS hospitals with excess readmissions, effective for discharges beginning on October 1, 2012. The regulations that implement this provision are in subpart I of 42 CFR part 412 (§412.150 through §412.154) (Readmissions Reduction Program, 2014) 34
Defined readmission as an admission to a subsection(d) hospital within 30 days of a discharge from the same or another subsection(d) hospital (see foot note 32) 35
(Rau, 2012) 36
See footnote 33 37
(Services, 2013) 38
(Geiger, 2012)
26
Conclusion The number of and occurrence of repeat EMS incidents in the City of Virginia Beach are increasing; these incidents are occurring at a positive rate of growth in which the observable years of provided data has shown annual increases. The rate of growth in non-repeat patients and non-repeat incidents inversely has shown a negative rate of growth and decline in occurrence. In other words, there are more repeat patients and repeat incidents occurring than standalone once a year EMS incidents. More service response is being directed in attending to patients that are seen more than once a year and for incidents that are of the same medical nature.
The majority share of care provided to these occurrences is also the most costly type of response service that can be provided in terms of personnel and unit hours. ALS and BLS transports are the primary response which results in cases involving a repeat incident. Emergency medical transports require unit hours and personnel of trained medical staff to meet these responses and have been determined as the greatest accountability of controllable cost in the EMS system.
Personnel and unit hours dedicated to repeat patients and repeat incidents may limit the ability of response to be dedicated to other emergency medical incidents and occurrences at a given time. Given the departments focus on efficiency and allocation of EMS services, this trend in recurring incident and patient rates should be considered as a threat to both efficiency and allocation, but as well to the safety and well-being of the community. Other EMS agencies across the country have implemented programs designed to curtail the occurrence of repeat incidents and reduce the number of repeat patients. These programs have provided real costs savings to not only the EMS agencies and their localities but as well have provided enhanced medical care to the benefit of the community by which they serve.
Given the passage of the Affordable Care Act and some of its provisions which tie Medicare reimbursement rates to patient care (e.g. readmission rates) and patient satisfaction (e.g. patient surveys), there exist economic incentive for hospitals to reduce the number of repeat incidents and patients through enforcement of financial penalties. The Department of EMS works in conjunction with the City of Virginia Beach’s healthcare providers and hospitals as both a provider of medical care and medical transport to medical facilities which serve the public at large. Taking this relationship into account, it would seem that the role in which EMS provides in-patient care and patient wellness management may become increasingly larger given the scope and scale of the requirements outlined in the Affordable Care Act.
How we identify repeat incidents and patients is just one component of many in addressing the concerns of public/health safety among the residents and visitors of Virginia Beach. Discovering the causes or contributing factors of these incidents and their repetitive occurrence, will be a challenge for EMS and the health care providers for years to come. As data collection and access to patient care outcome data improves,
27
the possibility to develop analytic endeavors that examine such causal linkages may yet be explored.
Collecting Patient Outcome Data
Currently, the Imagetrend patient medical reporting software has been very valuable to VBEMS in tracking data pertaining to the patients who are provided our services; however, what happens to those patients after they are transported to the ED is beyond our ability to capture their patient outcome data (was the transported patient admitted to the ED? How long was their stay? What was the cause of their injury? Etc.). Additional data pertaining to patient outcome of those patients treated and provided transport to ED’s would be of great benefit in allowing VBEMS to expand its training, research and program development operations. In turn, leveraging this provided data may be used to develop programs and initiatives that are aimed at reducing the number of readmission patients and low acuity patients to ED’s which may translate into a financial benefit for health care hospital systems/organizations.
HIPAA allows the patient outcome data to be shared with VBEMS of patients transported by VBEMS39.
Analyst’ Recommendations *The following statement is the opinion of the public safety analysts whom developed and executed the analysis of this report and the publication of its findings. The opinion of the analysts is not reflective of the Department of EMS’s stance or own developed recommendation to the issue addressed in this analysis*
Given the increasing growth in the rate of repeat incidents and repeat patients, along with the financial costs burdens to both the Department of EMS, the City of Virginia Beach and health care providers in the area (e.g. hospitals); the analysts’ recommends a cooperative approach to data collection and analysis be developed between EMS and hospital providers in the area. Given the nature of emergency medical services and the community it serves, it would be a proactive approach to develop a program that leverages existing resources to enhance service delivery and improve quality of life for the community.
Working with the City of Virginia Beach’s hospitals, patient admitted data and patient outcome data could be collected and used to discern relationships from variables that could be of greater benefit then what is currently available from the Imagetrend medical recording data managed by EMS. Working in a partnership with all the hospitals which receive EMS transported patients to improve the record and data collection pertaining to repeat incidents and the repeat patients whom are involved.
39
See appendix A
28
Given the financial incentives associated with hospitals under the Affordable Care Act, it would seem a negotiated agreement between EMS and the hospital networks in the city could be reached under the maxim of improving efficiency and enhancing service delivery. While a community paramedicine program would be the ideal like those examined in North Carolina, Fort Worth Texas and Eagle County Colorado; such a program would be putting the cart before the horse if you will. Further quantitative analysis should be first explored to determine the breadth and scope of the repeat incident and repeat patient occurrence happening in the City of Virginia Beach.
Exploring linkages and possible relationships through examination of patient outcome data and demographic variables may play a large role in helping shape the future development of clinical intervention programs such as paramedicine or community medicine.
29
Works Cited Banks, K. (2014, May 20). Chronic EMS users costing KC taxpayers millions, says fire chief. Kansas City,
Missouri.
CBS 4 Denver. (2011, October 06). Patients In Eagle County Program Get Care at Home. Retrieved June
10, 2014, from Denver.cbslocal.com: http://denver.cbslocal.com/2011/10/06/patients-in-new-
eagle-county-program-get-care-at-home/
Emergency Medical Services: Our Mission. (2014, June 05). Retrieved June 6, 2014, from The City of
Virginia Beach Web site: http://www.vbgov.com/government/departments/emergency-
medical-services/Pages/default.aspx
EMS, W. C. (n.a., n.a. n.a.). Adnanced Practice Paramedic Program. Retrieved June 10, 2014, from
ICMA.org:
http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=
0CDYQFjAC&url=http%3A%2F%2Ficma.org%2FDocuments%2FDocument%2FDocument%2F3020
00&ei=PBCXU-
XuKMvlsATMqIGYDw&usg=AFQjCNEQJb9Kiov1Lszt5XCljXt0iE_VNA&sig2=rUJtNciLaHrynoZBwidX
CQ&b
Expenditure Savings Analysis. (2014, May 09). Retrieved June 10, 2014, from Medstar911.org:
http://www.medstar911.org/Websites/medstar911/files/Content/1089414/MedStar_CHP_Econ
_Analysis_thru_4-30-14.pdf
Geiger, N. F. (2012, july). On Tying Medicare Reimbursement to Patient Satisfaction Surveys. Retrieved
June 10, 2014, from AJN American Journal of Nursing:
http://journals.lww.com/ajnonline/Fulltext/2012/07000/On_Tying_Medicare_Reimbursement_
to_Patient.2.aspx
Kavilanz, P. (2009, September 08). 911 abuse: Calling with the sniffles. Retrieved May 29, 2014, from
CNN Money:
http://money.cnn.com/2009/08/24/news/economy/healthcare_911_abuse/index.htm?cnn=yes
Kirkwood, S. (2009, January 16). Wake County Discusses Advanced-Practice Paramedic Program.
Retrieved June 10, 2014, from JEMS.com: http://www.jems.com/article/leadership-
professionalism/wake-county-discusses-advanced
Medstar. (2014, June 10). Retrieved June 10, 2014, from Medstar911.org:
http://www.medstar911.org/community-health-program
MOBILE INTEGRATED HEALTHCARE/COMMUNITY PARAMEDICINE LICENSURE REQUIREMENTS. (2014,
April 01). Retrieved Hune 10, 2014, from vdh.virginia.gov:
30
http://www.vdh.virginia.gov/OEMS/NewsFeatures/GuidanceDocument-
MoblieIntegratedHealthcare.pdf
NBC Action News. (2014, May 21). Repeat EMS callers cost tax payers millions. Retrieved from
https://www.youtube.com/watch?v=pNfBYG_SKFo
Rau, J. (2012, November 26). Hospitals Face Pressure to Avert Readmissions. The New York Times.
Readmissions Reduction Program. (2014, June 10). Retrieved June 10, 2014, from CMS.gov Centers for
Medicare & Medicaid Services: http://www.cms.gov/Medicare/Medicare-Fee-for-Service-
Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html
School, C. U. (NA, NA NA). 45 CFR 164.501 - Definitions. Retrieved March 01, 2014, from Legal
Information Institute: http://www.law.cornell.edu/cfr/text/45/164.501
Science Daily. (2011, April 15). Targeting top 911 callers can trim cost, improve patient care. Retrieved
May 29, 2014, from Science Daily:
http://www.sciencedaily.com/releases/2011/04/110414171046.htm
Services, C. f. (2013, October 24). The HCAHPS Survey-Frequently Asked Questions. Retrieved June 10,
2014, from CMS.gov: http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-
Instruments/HospitalQualityInits/Downloads/HospitalHCAHPSFactSheet201007.pdf
Sweigert, J. (2011, July 12). Trivial calls to 911 costly to taxpayers, investigation finds. Springfield, Ohio,
United States of America.
Virginia Performs. (2014, June 06). Retrieved June 06, 2014, from Virginia.Gov:
http://vaperforms.virginia.gov/indicators/healthFamily/lifeExpectancy.php
Virginia, C. o. (2006, January 01). 12VAC5-381-290. Home attendants. Retrieved June 10, 2014, from
http://leg1.state.va.us: http://leg1.state.va.us/cgi-bin/legp504.exe?000+reg+12VAC5-381-290
Virginia, C. o. (2014, June 10). CHAPTER 31 VIRGINIA EMERGENCY MEDICAL SERVICES REGULATIONS .
Retrieved June 10, 2014, from http://leg1.state.va.us: http://leg1.state.va.us/cgi-
bin/legp504.exe?000+reg+12VAC5-31
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Appendix
Appendix A
HIPAA
HIPAA allows the patient outcome data to be shared with VBEMS of patients transported by VBEMS: HIPAA Regulations Title 45 Subtitle A Subchapter C Part 164 Subpart E Section 164.506 45 cfr 164.506 “Uses and disclosure to carry out treatment, payment, or health care operations” Paragraph 4 A covered entity may disclose protected health information to another covered entity for health care operations activities of the entity that receives the information, if each entity either has or had a relationship with the individual who is the subject of the protected health information being requested. The protected health information pertains to such relationship, and the disclosure is:
(i)For a purpose listed in paragraph (1) or (2) of the definition of health care operations.
45 cfr 164.501 “Definitions”
Health care operations
(1)Conducting quality assessment and improvement activities, including outcomes evaluation and development of clinical guidelines…40
40
(School, NA)
32
Appendix B
Where did the data come from?
The data provided in this analysis was derived from the Department of EMS’s medical reporting software “Imagetrend”. Imagetrend is the software application and data warehouse that collects stores and reports on medical data that is recorded in the field and from the City’s CAD system. This data was culled manually by the public safety analyst for the department (Robert M. Davis) and reviewed for accuracy and validity before constructing data tables to present as findings within the research.
33
Addendum
GIS Spatial Mapping
Where to target outreach?
The analysis performed in this report via a statistical regression test, identified that a correlation was present between patient age and the occurrence rate of repeat EMS incidents. The statistical test resulted in patient age probabilistic being used to forecast the occurrence rate of repeat patient EMS incidents. Understanding and validating that a probabilistic relationship may be accounted for between patient age and the demand for repeat EMS services aids the organization in being able to determine what portion of the populace would benefit most from a targeted community outreach program that provided preventative/follow-up medical care to retard the occurrence rate of repeat EMS incidents.
The next step was to then determine “where” such outreach initiatives would have the greater probabilistic rate of providing the most utility in addressing repeat EMS incidents; to accomplish this, a geospatial information systems mapping program was used to spatially map repeat EMS incidents along population demographic characteristics in the City of Virginia Beach. This technique is known as “spatial analysis” or “density” or “heat” mapping. The rate of repeat EMS incidents per 1sq. mile are mapped as points on a 2D map plane; the higher the density rate of those points, the heavier the shade of color used to indicate where clusters occur. This is all overlaid on top of a map which breaks the City of Virginia Beach by U.S. Census districts; the heavier the shade, the higher propensity of the population within that district is of a particular age group.
This enables a visual correlation between age and repeat EMS incidents; as well, it identifies visually, where repeat EMS incidents are occurring at the highest rate. In the maps presented, the dispatch complaint of “Fall” was used to identify repeat EMS incident rates; Fall dispatches comprise roughly 13% of repeat incidents and have been identified as one of the highest injury causing EMS events among the population age 60 and older.
34
Figure 18: Fall density per square mile (left) and population 60 and over by census tract (right)
The map on the left illustrates a density map of repeat Fall EMS incidents; the map on
the right illustrates a density map of population age groups by census tract. As can be
visually discerned, there is a visual correlation between age and the occurrence of
repeat EMS Fall incidents.
Operational personnel, EMS medics and other stakeholders vested with the Department
of EMS made mention that there are a number of retirement and senior care facilities
across the City of Virginia Beach and that a number of EMS medics stated that there
are often times where they will pick up a patient at one of these facilities only to be
35
dispatched immediately to the same facility for another patient following transport to the
ED. To determine if there might be a spatial correlation between repeat Fall incidents
and these senior facilities, another density map was used and overlaid with a map
identifying individual retirement, senior care facilities in the City of Virginia Beach.
Figure 19: Nursing home locations pin mapped and nursing homes overlaid with fall density per square mile
36