Utilization prediction for helicopter emergency medical services

ORIGINAL CONTRIBUTION EMS, helicopter, prediction of use; helicopter, EMS, prediction of use

Utilization Prediction for Helicopter Emergency Medical Services As the number of helicopter emergency medical services (HEMS) programs continues to expand rapidly, the need for an accurate, readily obtainable, inexpensive method of utilization prediction .for these services has become apparent. Accurate volume and case m i x prediction for these services are increasingly important as financial constraints become more severe. All previous methods of utilization prediction based on experiences of individual services or accident statistics have been either inaccurate, costly, or difficult to obtain in a relatively short period of time. Prediction of HEMS utilization requires consideration of m a n y significant, simultaneous factors, in addition to patient needs based on population statistics. Through use of a survey of all hospital-based helicopter emergency service programs and published census data, this study analyzed factors relating to helicopter program volume and case mix, providing insight as to why previous methods utilizing total population were inaccurate. A more accurate yet simple and inexpensive method of utilization prediction for HEMS was developed. [Macione AR, Wilcox DE: Utilization prediction for helicopter emergency medical services. Ann Emerg Med April 1987;16:391-398.]

INTRODUCTION Prediction of aeromedical helicopter utilization previously has been limited to educated guesswork. Initial estimation of "need" was based on incident rates of various diseases or injuries obtained from n a t i o n a l or state statistics. Rhee et ab enumerated the intricate factors involved in "need" estimation. This m e t h o d of "utilization" prediction frequently is inaccurate due to unique local demographics and environmental factors. It has likewise been demonstrated that projections based on the experience of cities or areas with "similar" p o p u l a t i o n characteristics are also inaccurate due to gross over-simplification, because m a n y variables affect utilization to a greater extent than does population. A rapid increase in development of sophisticated and expensive hospitalbased helicopter transport services z began in 1972, and has continued unabated to date, with 90 programs operating now. By January 1985, only nine states remained w i t h o u t hospital-based helicopter programs. Three of these had access to non-hospital-based helicopter e m e r g e n c y m e d i c a l services (HEMS) and five are located in the northeastern United States w i t h i n the service area for N e w England Life F l i g h t / U n i v e r s i t y of M a s s a c h u s e t t s - Worcester. Expansion of service occurs w i t h predictable regularity after the first aeromedical helicopter is established in a state (Figure). There are now several areas throughout the country where multiple services compete for patient transport. Because the inequity between "need" and "utilization" has become apparent, we undertook a national survey of hospital-based HEMS to determine factors that best predict transport volume and case mix. By evaluating the nationwide experience of actual utilization, a correlation matrix of program variables and best predictors can be developed. The same m e t h o d was used by New England Life Flight/UMass-Worcester in its successful presentation to the Public Health Council of the C o m m o n w e a l t h of Massachusetts to justify an extension of its provisional D e t e r m i n a t i o n of Need. 3

16:4 April 1987

Annals of Emergency Medicine

AR Macione, RN, PhD David E Wilcox, MD, FACEP Worcester, Massachusetts From the New England Life Flight, University of Massachusetts Medical Center, Worcester, Massachusetts. Received for publication October 23, 1986. Accepted for publication December 10, 1986. Presented at the University Association for Emergency Medicine Annual Meeting in Portland, Oregon, May 1986. Address for reprints: David E Wilcox, MD, FACEP, Emergency Department, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, Massachusetts 01605..

391/37

HELICOPTER EMS USE Macione & Wilcox

METHODS During the winter of 1984-1985, we undertook a national survey of all hosp i t a l - b a s e d HEMS in the U n i t e d States. Initial information on these services was obtained from the American Society for Hospital-Based Emergency Aeromedical Services (ASHBEAMS), the National Flight Nurses' Association (NFNA), and the journal Hospital Aviation. Demographic information concerning each survey respondent was obtained from the US Bureau of Census. 4-8 Data included in this study were l i m i t e d to p r o g r a m s at l e a s t 12 m o n t h s old as of September 1984. This restriction minimized skewing of the combined data by new and still developing programs. Each helicopter service was asked to summarize data through September 30, 1984. There were two follow-up mailings and three follow-up phone calls to maximize the response rate. The intent of the study was to determine the program demographics and characteristics that could best predict program or helicopter volume and case mix. Program variables selected empirically for predictive potential included the following: total population served (POPULN); standard metropolitan statistical area (SMSA); SMSA u r b a n p o p u l a t i o n (SMSAUPOP); SMSA rural population (SMSARPOP}; SMSA square miles (SMSASQM); SMSA p o p u l a t i o n density (SMSADSN); program age (PGMAGE); total number of dedicated trauma ICU beds at the base hospital INMBDS); program marketing budget (MKTBGT); time spent by program personnel' for first responder education (TMED); and a single dicotomous variable, number of helicopters per program (single versus multiple). The program utilization factors we wished to predict included: average number of flights per month (AVFLMO), average number of flights per helicopter per month (ATCFLCRFT), total number of trauma patients transported directly from the scene of the accident per month (NOSCTR), total n u m b e r of cardiac p a t i e n t s transported per m o n t h (NOCARD), and total n u m b e r of pediatric patients transported per month (NOPEDTOT). All data were analyzed using frequencies, means, standard deviations, correlations, regression, ANOVA, chisquare analysis, and t test. The Pierson product moment correlation was used to evaluate these variables. 38/392

TABLE 1. Minimum and maximum value ranges for selected predictors of

hospital-based helicopter programs Value Range Variable Label (Name) Average no. of flights per month (AVFLMO) No. of helicopters in use (NMBCRFT) Average flights per craft per month (AVFLCRFT) Program age in months as of 9/30/84 (PGAGE) Number all trauma per month (NOTOTTR) Number on-scene traumas per month (NOSCTR) Number of pediatric traumas per month (NOPEDTR) Number of neurological trauma patients per month (NONTR) Number of cardiac patients per month (NOCARD) Number of neonates per month (NONEOTOT) Number of pediatrics per month (NOPEDTOT) Number of other cases per month (NOOTTOT) All trauma as % total flights (TOTTR) On-scene trauma as % of total flights (scm) Pediatric trauma as % of total flights (PEDTR) Neurological trauma as % total flights (NTR) Cardiac patients as % of total flights (CARDTOT) Neonates as % of total flights (NEOTOI-) Pediatrics as % of total flights (PEDTOT) All other cases as % of total flights (OTTOT) SMSA urban population (SMSAUPOP) SMSA urban square miles (SMSAUSQ) SMSA rural population (SMSARPOP) SMSA rural square miles (SMSARSQ) SMSA rural population density (SMSARDN) Percentage of down-time: Weather (DWNTW) Percentage of down-time: Mechanical (DWNTMC) % of patients to other hospitals (OHTRPPCT) Hours per month first responder training (TMED) No. trauma ICU beds (NMBDS)

Annals of Emergency Medicine

Minimum

Maximum

24 1

310 5

24

119

12

110

3

139

1

133

1

39

0

19

1

6o

0

5O

0

71

0 10

48 87

1

83

2

41

0

35

4

51

0

5o

0

4O

0

50

21,209

7,392,175

0 5,966

604 355,709

452

8,803

2.4

167.1

0

15

0

7

O

9O

0 0

88 36

16:4 April 1987

TABLE 2. Predictors (correlations) for selected program characteristics Selected

Best

Worst

Variables*

AVFLMO

AVFLCRFT

NOTOTTR

NOSCTR

NOCARD

NOPEDTOT

SMSARSQ .52§

SMSASQM .39:~

SMSASQM .30

PGMAGE .351-

SMSARSQ .42:~

POPULN .51 :~

SMSASQM .49§

SMSARPOP .37:~

SMSARSQ .30

SMSASQM .32t

SMSASQM .41 :~

SMSAUP©P ,50:~

PGMAGE .48§

SMSARSQ .34t

PGMAGE .21

SMSARSQ .31t

PGMAGE .36t

SMSASQM ,36t

NMBDS ,42:~

TMED - .26

SMSARPOP .18

SMSARPOP .23

NMBDS ,32t

PGMAGE .35t

SMSARPOP .38¢

POPULN .25

TMED .11

NMBDS ,14

SMSARPOP .25

SMSARSQ .30

POPULN .24

SMSAUPOP .23

SMSADNS -.09

MKTBGT - . 10

MKTBGT .22

SMSARPOP .30

SMSAUPOP .22

MKTBGT .22

NMBDS - ,06

SMSADNS - .09

SMSADNS - ,21

SMSADNS .25

MKTBGT .19

PGMAGE ,21

SMSAUPOP ,03

POPULN .06

TMED -.21

NMBDS -,17

TMED -.18

NMBDS .11

MKTBGT - .01

SMSAUPOP .05

SMSAUPOP - .01

TMED - . 13

SMSADNS .05

SMSADNS .05

POPULN ,002

TMED .03

POPULN .0,02

MKTBGT .04

tSignificant at .05 level. :~Significant at .01 level. §Significant at .001 level. *MKTBGT, marketing budget for program; NMBDS, number of beds dedicated to trauma ICU at base hospital; PGMAGE, program age as of 9/30/84; POPULN, population of program SMSA, total; SMSADNS, population density (persons per square mile) of program SMSA; SMSASQM, total square miles of program SMSA; and SMSAUPOP, urban population of program SMSA,

FIGURE. Growth rate by number of

helicopters by time since inception of first HEMS per state.

10 9

2

8

N

z

;

6

0

d Z

3 2

I 1

I 2

I 3

I 4

I 5

I 6

I 7

I 8-12

Time (yr)

RESULTS A N D D I S C U S S I O N Forty-nine completed responses were received from the 69 programs meeting the age requirements, for a 16:4 April 1987

response rate of 71%. The range of values obtained for each variable is shown (Table 1). Although civilian helicopter aeromedical transport was Annals of Emergency Medicine

derived from the American military experience, and although t r a u m a transports are emphasized across the country, data collected indicates that the majority of hospital-based helicopter transports performed in the United States are for non-trauma patients. A correlation matrix of the ten best predictors for selected variables is shown (Table 2}. Asterisks indicate significance at the various confidence levels as indicated. The best overall predictor for program size or patient transport volume was the SMSA rural square miles served by the helicopter program. Predominately rural services covering large areas or inaccessible areas are far more likely to have high volumes t h a n are p r o g r a m s s e r v i n g predominately urban areas regardless of the total population served. Urban programs serving large surrounding 393/39

HELICOPTER EMS USE Macione & Wilcox

TABLE 3. Means of selected variables by levels of SMSA rural square miles* All Level Level Level Level Programs 1 2 3 4 62 49 34 58 96 44 46 34 40 53 28 25 14 24 49 (47.5%) (51%) (40%) (42%) (51%) Total no. scene traumas per month 14 7 7 10 29 (22%) (14%) (21%) (18%) (30%) Total no. cardiac patients per month 13 9 8 17 18 (21%) (19%) (23%) (25%) (19%) Total no. pediatric patients per month 9 5 4 9 15 *Levels of SMSA rural square miles. Level 1, 0000 - 1,250 square miles; Level 2, 1,251 - 2,000 square miles; Level 3, 2,001 - 3,500 square miles; Level 4, 3,501 and up. Program Variables Average no, flights per month Average flights per craft per month Total no, traumas per month

TABLE 4. Means of selected variables by number of helicopters m program

Program Variables Average no. flights per month Average no. craft per month Total no. traumas per month Total no. scene traumas per month Total no. cardiac patients per month Total no. pediatric patients per month

Mean All Programs 62 44 28 (47.5%) 14 (22%) 13 (21%) 9

Program Means by Number of Craft One Multiple 4O 136 40 57 21 49 (49%) (42%) 9 30 (23%) (20%) 9 26 (21%) (23%) 5 21

TABLE, 5. Means of selected variables by age of program

Mean Program Variables Average no. flights per month Average no. craft per month Total no. traumas per month Total no. scene traumas per month Total no. cardiac patients per month Total no. pediatric patients per month

rural areas will be more heavily utilized than urban programs serving smaller rural areas, regardless of urban 40/394

All Programs 62 44 28 (47.5%) 14 (22%) 13 (21%) 9

Means for Programs with Ages 30 mo 31.5 30 17 (48%) 5 (19%) 7 (20%) 5

population. The increasing numbers of patients transported with increasing SMSA rural square miles covered Annals of Emergency Medicine

31-60 mo 73 52 40 (53%) 22 (26%) 15 (20%) 9

61-180 mo 90 53 24 (39%) 16 (23%) 18 (24%) 14

is shown (Table 3). Level 1 programs do not appear to follow the pattern, but the small number of programs 16:4 April 1987

TABLE 6. Means of selected variables by levels of program SMSA rural population* All Programs

Program Variables Average no. flights per month Average no. craft per month Total no. traumas per month Total no. scene traumas per month Total no. cardiac patients per month

62

Level 1

Level 2

36

60

Level 3 71

Level 4 81

44 28 (47.5%) 14 (22%)

36 21 (58%) 11 (19%)

46 25 (41%) 11 (19%)

45 28 (40%) 13 (18%)

48 37 (46%) 19 (23%)

13 (21%)

15 (25%)

15 (25%)

13 (19%)

16 (20%)

Total no. pediatric patients per month 9 6 6 15 12 *Levels of program SMSA rural population. Level 1, 4,000 - 45,000; Level 2, 45,001 - 72,000; Level 3, 72,001 - 168,000; Level 4, 168,001 - 500,000.

TABLE 7. Means of selected variables by levels of program population*

Program Variables

All Programs

Level 1

Level 2

Level 3

Level 4

Average no. flights per month Average no. craft per month

62 38 58 66 91 44 38 47 43 49 Total no. traumas per month 28 22 26 28 37 (47.5%) (53%) (46%) (44%) (47%) Total no. scene traumas per month 14 7 12 11 25 (22%) (23%) (24%) (19%) (24%) Total no. cardiac patients per month 13 8 16 18 11 (21%) (22%) (26%) (23%) (14%) Total no. pediatric patients per month 9 9 6 9 19 *Levels of population in SMSA. Level 1, ~< 275,000; Level 2, 275,000 - 807,000; Level 3, 807,001 - 1,630,000; Level 4, 1,630,001 7,400,000.

falling into this level m a y h a v e skewed the data. Single helicopter versus multiple helicopter programs are c o m p a r e d (Table 4). In all cases, multiple helicopter programs not only had greater total volume, but also had increased volume per craft. In general, multiple helicopter programs are older, more established, and had their own built-in backup capability to reduce missed flights. The case mix remained virtually unchanged despite the number of craft. Single-craft programs averaged 40 flights per month, while multiple helicopter programs leveled off at 57 flights per month per craft. The growth of helicopter programs with increasing age is shown (Table 5). As programs grow and mature, they tend to add additional craft and level off at 52 to 53 flights per month per 16:4 April1987

craft. The "age of maturity" appears to occur after approximately three years of service. With increasing age, nontrauma transports increase volume at a m o r e rapid rate t h a n do t r a u m a transports. SMSA rural population is a good predictor for program volume but does not alter case mix {Table 6). SMSA total population is a less reliable predictor and does not reach statistical significance (Table 7). This may be due to the grouping of programs in the lower three levels, with relatively narrow population limits, and the smaller number of programs in Level 4, with a wide range of population limits. As population increases, there tends to be an increase in the number of crafts serving the area, with the result that although total flights increased, the number of flights per craft remained Annals of Emergency Medicine

the same. An analysis of SMSA rural population density (Table 8) indicates that this is a good predictor up to a certain level of population density. We theorize that as rural population density increases above this threshold, hospital density and helicopter program density as variables also will increase. Hospital density and helicopter program density were not evaluated as variables in this study. The percentage of trauma transports originating from the scene of an accident decreased as rural p o p u l a t i o n density increased. We theorize that as rural population density increases, a parallel increase in hospital density reduces the likelihood that the helicopter can reach the scene of an accident more rapidly than the patient can be transported to a local hospital. A1395/41

HELICOPTER EMS USE Macione & Wilcox

TABLE 8. Means of selected variables by program levels of SMSA rural density*

Program Variables Average no. flights per month Average no. craft per month Total no. traumas per month

All Programs 62 44 28 (47.5%)

Level 1 51 37 28 (54%)

Level 2 71 46 27 (38%)

Level 3 88 54 33 (38%)

Level 4 42 40 23 (55%)

Total no. scene traumas per month

14 (22%)

16 (32%)

15 (21%)

12 (14%)

8 (19%)

Total no. cardiac patients per month

13 (21%)

12 (23%)

13 (19%)

18 (20%)

8 (20%)

Total no. pediatric patients per month 9 4 14 16 3 *Levels of SMSA rural density (number of persons per square rural mile). Level 1, 10 - 22; Level 2, 23 - 40; Level 3, 41 - 70; Level 4, 71 - 168.

TABLE 9. Means of selected variables by number of dedicated trauma ICU beds Program Variables Average no. flights per month Average no. craft per month Total no. traumas per month Total no. scene traumas per month Total no. cardiac patients per month Total no. pediatric patients per month

t h o u g h m u c h e m p h a s i s has b e e n placed on off-scene transport, time to definitive care is the critical factor for trauma patients. W h e t h e r the helicopter picked up the patient at the scene or met the patient at the local hospital is immaterial, as long as the patient reaches definitive trauma center care rapidly. We have coined the phrase "modified scene" to distinguish trauma patients met at local hospitals and rapidly transported to regional trauma centers within an hour from those receiving delayed interhospital transport. The total number of dedicated trauma ICU beds at the base hospital, the next hypothetically i m p o r t a n t variable, is shown (Table 9). The majority of helicopter programs transported the m a j o r i t y of their patients to their homebase hospital. In these programs, there was no correlation between the total number of dedicated trauma ICU beds and the number of trauma pa42/396

All Programs 62 44 28 (47.5%) 14 (22%) 13 (21%) 9

0 53 45 28 (46%)

37 27 (53%)

10 (19%) 11 (18%)

16 (29%) 8 (20%)

7

8

tients transported. In fact, w i t h increasing hospital size and increasing n u m b e r s of dedicated t r a u m a I C U beds, although flight program volume tended to be higher, the percentage of trauma transports dropped while the percentage of cardiac and pediatric t r a n s p o r t s increased. Perhaps dedicated regional t r a u m a centers also tended to have dedicated resources as tertiary cardiac and pediatric referral centers. The amount of time flight program p e r s o n n e l dedicate to e d u c a t i o n of first responders and the flight program m a r k e t i n g budget, respectively, are s h o w n (Tables 10, 11). N e i t h e r is a good predictor, and the first responder education time becomes a negative predictor for multiple craft programs. We theorize that while early public relations efforts and first responder education are necessary to establish a program's t r a u m a base, as a p r o g r a m matures less public relations and first Annals of Emergency Medicine

1-12 49

13-40 145 60 29 (31%) 20 (11%) 33 (30%) 23

responder education effort is necessary to maintain program growth. The N e w E n g l a n d Life F l i g h t / U M a s s Worcester experience corroborates this hypothesis. During the second year of our program we quintupled our firstresponder educational programs and observed a tripling in the percentage of off-scene trauma transports. While this level of first responder educational activity has not been maintained, our percentage of off-scene activity has continued to increase. In 1981, the Massachusetts State H e l i c o p t e r Task Force Report predicted a need for 2,400 aeromedical helicopter transports per year in the C o m m o n w e a l t h of M a s s a c h u s e t t s . Actual utilization has demonstrated a very different volume level. Whatever the reason for this difference, such v a r i a t i o n has o c c u r r e d r e p e a t e d l y across the country. With the data collected and analyzed in this study, New England Life 16:4 April 1987

TABLE 10. Means of selected variables by program levels of hours educating first responders*

Program Variables Average no. flights per month Average no. craft per month Total no. traumas per month Total no. scene traumas per month Total no. cardiac patients per month

All Programs 62 44 28 (47.5%) 14 (22%) 13 (21%)

Level 1 60 50 24 (40%) 9 (15%) 13 (22%)

Level 2 94 53 45 (48%) 30 (32%) 21 (22%)

Level 3 58 4O 28 (49%) 13 (23%) 13 (23%)

Level 4 50 38 24 (47%) t7 (34%) 7 (15%)

Total no. pediatric patients per month 9 18 12 10 6 *Levels of hours educating first responders per month (eg, police, fire, ambulance, etc). Level 1, 0 - 2 hours per month; Level 2, 3 - 9 hours per month; Level 3, 10 - 13 hours per month; Level 4, 14 - 88 hours per month.

TABLE l l . Means of selected variables by levels of program marketing budget*

All Level Level Level i_evel Programs 1 2 3 4 62 51 41 91 55 44 39 41 62 46 28 22 19 46 19 (47.5%) (44%) (47%) (50%) (35%) Total no. scene traumas per month 14 11 10 17 5.5 (22%) (21%) (24%) (19%) (10%) Total no. cardiac patients per month 13 14 10 20 14 (21%) (28%) (24%) (22%) (25%) Total no. pediatric patients per month 9 10 4 9 5 *Levels of program marketing budget. Level 1, under $12,000; Level 2, $12,001 - $20,000; Level 3, $20,001 - $50,000; Level 4, $50,001 $9O,0OO. Program Variables Average no. flights per month Average no. craft per month Total no. traumas per month

Flight/UMass-Worcester has thus far accurately predicted its continued volume growth and changing case mix.2 We also were able to accurately correct the predicted flight v o l u m e and case mix for t h e n e w B o s t o n Med Flight program, which had been projected inaccurately from i n f o r m a t i o n on population, urban population, and "need" estimation. 2 CONCLUSION SMSA rural square miles, SMSA square miles, and SMSA rural population are the best predictors for helicopter program v o l u m e , i n c l u d i n g average flights per m o n t h and average flights per craft. With increasing program age, t h e average f l i g h t s per month will increase. Often, the percentage of n o n t r a u m a transports increases over t r a u m a transports, b u t 16:4 April 1987

this is less consistent and is dependent on local factors. None of the variables in our study reached statistical significance for predicting total trauma t r a n s p o r t s . The total service area p o p u l a t i o n and SMSA u r b a n population are n o t good predictors for program volume or case mix, with the exception of pediatric transports. The "age of maturity" for hospital-based HEMS programs appears to occur after three years of service. After that time, the average program will perform 45 to 60 flights per m o n t h per craft, slightly less t h a n 50% of the flights w i l l be t r a u m a transports, and 15% to 25% of all flights will be directly from the scene of an accident. The ability to predict flight program u t i l i z a t i o n by a validated statistical method will significantly improve the

Annals of Emergency Medicine

ability to locate helicopter programs at justifiable sites and will eliminate the need for "educated guesswork." Aeromedical transport programs can now be located in areas in which utilization will justify the expense, and areas already at m a x i m u m utilization can be spared unnecessary duplication of services.

REFERENCES 1. Rhee KJ, BurneyRE, MackenzieJR, et al: Predicting the utilization of helicopter emergency medical services: An approach based on need. Ann EmergMed 1984;i3:916-923. 2. Medical News editorial: Helicopters and other air ambulances. JAMA 1985;253:24692477. 3. MacioneAR, WilcoxDE: Assessmentof Utilization and Need. Report to the Massachusetts Department of Health, Division of Determination of Need for EMS Helicopter Transport, New England Life Flight/UMass-Worcester, March 15, 1985. 397/43

HELICOPTER EMS USE Macione & Wilcox

4. American Hospital Association: American Hospital Association Guide to the Health Care Field (1983 Edition). Chicago, Illinois, 1983.

6. US Bureau of the Census: State and Metropolitan Area Data Book: 1982. Washington, DC, US Bureau of the Census, 1983.

8. US Bureau of the Census: Census of the Population: i980. Washington, DC, US Bureau of the Census, 1983.

5. US Bureau of the Census: County and City Data Book: 1983 ed 10. Washington, DC, US Bureau of the Census, 1983.

7. US Bureau of the Census: Statistical Abstract of the US: 1984, ed I04. Washington, DC, US Bureau of the Census, 1984.

9. Macione AR, Wilcox DE: Appropriateness of utilization: A method of assessment. Hospital Aviation 1985;4:33-38.

American Board of Emergency Medicine Notice On June 30, 1988, the practice option will terminate for those physicians wishing to meet the credential requirements of the American Board of Emergency Medicine's certification examination. Practice, teaching, or CME accumulated after the above date may not be used to satisfy the practice requirements. Questions should be directed to ABEM, 200 Woodland Pass, Suite D, East Lansing, MI 48823; 517/332-4800.

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