Frequency of Emergency Medical Service contacts after hospital admissions

doi:10.21203/rs.3.rs-4130667/v1

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Frequency of Emergency Medical Service contacts after hospital admissions

https://doi.org/10.21203/rs.3.rs-4130667/v1

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Background

Identifying frequent users of Emergency Medical Services (EMS) in the post-discharge period can potentially direct interventions to prevent deterioration at home. This study aimed to describe the frequency of post-discharge emergency phone calls within 30 days after common medical and surgical categories of hospital admission.

Methods

A retrospective cohort study retrieved data from the electronic medical record and the EMS Capital Region Denmark database after approval by the Danish Health Data Authority. The study aimed to investigate number of 30-day EMS calls per 1,000 days alive outside hospital in patients hospitalized due to acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and colorectal surgery as compared to 18 other disease categories. We hypothetized that patients discharged after hospitalization for AECOPD or colorectal surgery had higher number of EMS calls than the remaining categories.

Results

We included 16,338 patients with a discharge from hospital between August 2021 and August 2022.The overall number of EMS calls was 4,263 with 9.1 (95% confidence interval (95%CI)): 8.8 to 9.4) calls per 1,000 patient days within 30 days. Patients discharged after hospitalization due to AECOPD contacted EMS 14.9 (95%CI: 13 to 16) times per 1,000 patient days. Patients undergoing colorectal surgery had an EMS call frequency of 7.5 (95%CI: 6.4 to 8.7) calls per 1,000 patient days.

Conclusion

Patients discharged after hospitalization due to AECOPD had a higher 30-day EMS call frequency compared with other medical cohorts, whereas a more even pattern of contacts was seen after discharge from various surgical admissions.

Most healthcare systems are currently struggling with the challenge of striving for increased efficiency while operating within tight budgets[1]. Hospital crowding is a prevalent issue arising from various complex factors and resulting in compromised patient care and adverse events such as increased mortality[2]–[5]. Despite political reforms, including reorganization of Danish hospital structure in 2007, readmission rates in Denmark have still demonstrated increasing trends[6]. Increasing readmission rates may contribute to hospital crowding, and European studies report 30-day readmission rates of 10–20% among patients aged 65 years and older after medical or surgical hospitalization[7]–[11].

Danish citizens are required to obtain a referral from general practitioners, or the Emergency Medical Service (EMS) before any new hospital contact after 24 hours from the time of primary hospital discharge. In 2020, EMS in the Capital Region of Denmark received 1,168,382 calls including ambulance dispatch in 103,389 of the inquires. Emergency ambulance transport (priority A) accounted for 41.7% of the dispatch responses[12]. Frequent EMS use can be indicative of underlying medical complications, unmet healthcare needs, or poor care transitions[13]–[15], all of which may contribute to a higher likelihood of readmissions or severe complications. Nationwide attention is given to COPD among the medical diseases, as these have readmission rates up to 26% [16]. Colorectal surgery likewise constitute a typical large group of surgical patients with moderate to high risk of adverse events and readmission[17], [18]. The characteristics of which patients that have the most frequent post-discharge EMS contact are, however, sparsely elucidated

The aim of this study was to describe the frequency of post-discharge emergency phone calls within 30 days after common medical and surgical categories of hospital admission. We hypothetized that patients discharged after hospitalization for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) or after colorectal surgery had higher number of EMS calls than the remaining categories.

Study Design

This was an observational retrospective cohort study on data from the electronic medical record (Sundhedsplatformen (SP), Epic Systems, Verona, Wisconsin, USA) and the Copenhagen Emergency Medical Service database. The study was approved by the Danish Health Data Authority (Journal No. 22035235, 15 September 2022) prior to data collection and analysis.

Setting

We included patients ≥ 18 years, who were discharged from a hospital in the Capital Region of Denmark (1,900,000 inhabitants) from August 1, 2021 to July 31, 2022. In case of multiple hospital admissions, the first hospital discharge was used. All patients were registered by a diagnosis code (SKS-code) within the Danish version of the International Classification of Diseases (ICD-10 system)[10], and patients were included if registered by a SKS-code within one of 20 diagnosis groups. Inquiries to the EMS were registered by a criteria code defined by a nationwide criteria-based Emergency Medical Dispatch System (Danish Index for Emergency help)[12] from which we selected five symptom groups in order to define the criteria for patient contacts. We included four groups with the highest proportion of referrals to hospital in 2020[12] and one group of undecided symptoms. The response to the EMS contacts was determined using urgency prioritization algorithms from the National Emergency Medicine System, defined as response A for urgent and lifethreatening cases with or without an emergency doctor, response B for urgent but not lifethreatening cases with or without lights and sirens, and response C-D-E for non-urgent cases.

All Danish citizens have a unique ten-digit civil registration number (CPR) that can be used to link all data in any of the Danish national registries [19]. Individual-level data from SP and EMS were linked using the CPR number. Patients were excluded if they had primary address outside the Capital Region of Denmark.

Variables

The exposure variables was each of 20 prespecified SKS-groups (appendix 1). We aimed to include 1000 patients pr. group selected at random by an indedependent data manager. Patients were classified based on the main SKS-code registered upon primary index hospitalization. If a patient had several primary admission codes during a hospital admission (e.g., arising from sequences of admissions to different hospital wards), we used the first of primary admission codes. This was also applied to readmissions. Readmission was defined as hospitalization at least 24 hours after discharge, with a duration of at least 24 hours. Patients with multiple contacts were included, but if a patient had two contacts within three hours or less, they were regarded as one contact. Prioritization of calls followed the ABCDE approach with calls concerning airway issues given precedence. The co-primary exposure variables were index hospitalization with the primary diagnoses of AECOPD or index hospitalization for colorectal surgery. These two groups served as reference groups for the other 18 medical and surgical groups, representing the secondary exposure variables.

The primary outcome was the number of 30-day EMS calls per group per 1,000 days alive outside hospital. Secondary outcomes were the overall criteria for EMS contact defined as symptoms from either the central nervous system, chest / heart, respiratory system, gastrointestinal system or symptoms defined as ‘other’. Explorative outcomes were number of calls to the out-of-hours, non-emergency medical helpline 1813 or emergency 1-1-2 calls (for urgent medical assistance because of acute lifethreathening illness or injury); the EMS response to calls defined as either response A, B or C-D-E; and 30-day frequency of readmission and mortality.

Statistics

Data is presented with descriptive statistics as appropriate. Outcome data is calculated for each of the 20 SKS-groups, and number of EMS calls is analyzed as rates and described by mean estimates with 95% confidence intervals. Association between outcome data and the reference groups is reported as mean difference with 95% confidence intervals calculated using confidence intervals for Poisson counts or rates and compared using two sample proportions test in all groups. Readmission and mortality rates are analyzed as dichotomous variables with unadjusted odds ratios and compared by Fischer’s exact test. A p value < 0.05 was considered statistically significant.

Data were analyzed using R version 4.1.2 (R Core Team, Vienna, Austria).

In 2020, the Capital Region experienced 248,658 somatic hospital admissions[20]. Data from 18,625 patients were screened during the 1-year period with data from 16,338 unique patient records included in the final analysis (Fig. 1). Of those, 2,678 patients called the EMS within 30 days after discharge, with 690 patients calling 1-1-2 and 1,730 patients calling 1813.

The patients who contacted the EMS had a median age of 73 years (IQR: 60–81) and 48% were women (Table 1). Twenty six percent of them had more than three comorbidities and a median of 34 medical administrations during their hospitalization. The patients reached a maximal National Early Warning Score (NEWS)[21] of 5 (IQR: 3–5) during index hospitalization.

Table 1

Population baseline characteristics
	EMS Contact (N = 2678)	No EMS Contact (N = 13660)	Overall (N = 16338)
Age (years)	73 [60 to 81]	69 [57 to 78]	70 [58 to 78]
Female sex (%)	1,295 (48%)	6,772 (50%)	8,067 (49%)
Comorbidities 0 1 2 3 ≥ 4	915 (34%) 447 (17%) 350 (13%) 267 (10%) 699 (26%)	7,559 (55%) 2,224 (16%) 1,398 (10%) 900 (7%) 1,579 (12%)	8,474 (52%) 2,671 (16%) 1,748 (11%) 1,167 (7%) 2,278 (14%)
Number of administered medications during index admission*	34 [21 to 50]	25 [14 to 38]	26 [15 to 41]
Median NEWS value during index admission**	1 [0 to 2]	1 [0 to 2]	1 [0 to 2]
Max. NEWS value during index admission	5 [3 to 8]	3 [2 to 5]	3 [2 to 6]
Latest NEWS value before discharge	1 [0 to 2]	0 [0 to 1]	1 [0 to 2]
Length of index admission (days)***	5 [1.8 to 14]	4 [1.2 to 11]	4.3 [1.3 to 12]

Data is presented as medians with [IQR] and N (%). NEWS: National Early Warning Score. * Missing data is 14% due to no registration of medicine. **Missing data is 17% for EWS data due to no complete scoring. *** Missing data is 13% due to registration of 0 seconds admission time. EMS = Emergency Medical Services.

Outcome data

The cumulated number of EMS calls for the 20 prespecified groups was 4,263 equivalent to 9.1 (95% CI: 8.8 to 9.4) calls per 1,000 patient days within 30 days after discharge. Patients discharged after hospitalization due to AECOPD (n = 971) contacted EMS 15 (95%CI: 13 to 16) times per 1,000 patient days (Table 2). Among the nine medical exposure groups, seven demonstrated a significantly lower frequency of EMS calls compared to the AECOPD group (Table 2). Patients discharged after treatment for sepsis demonstrated the significantly highest call frequency (19 (95% CI: 17 to 21) calls, mean difference 4.1 (95%CI: 1.8 to 6.4)) compared with the AECOPD group. Patients discharged after colorectal surgery (n = 914) demonstrated an EMS call frequency of 7.5 (95%CI: 6.4 to 8.7) calls per 1,000 patient days.

Within the surgical exposure groups, only patients admitted for gynecological surgery and lung- and mediastinal surgery had significantly lower EMS call frequency compared with the colorectal resections group. Seven groups demonstrated a significantly higher frequency of EMS calls (Table 2). The frequency of calls were evenly distributed across the entire region with call rates of 25–30 calls per 100 patients per 30 days in most municipalities (appendix 2). The distribution of symptoms leading to EMS inquiries varied across the groups with 38.6% of inquiries pertaining to the category “gastrointestinal symptoms” (appendix 3). Seventy three percent of inquiries from the AECOPD group were related to respiratory symptoms (Fig. 2). Response A and B comprised the vast majority of responses with 79.6% (Fig. 3).

Table 2

– outcome data
	N patients	N calls	N patient days	Calls pr. 1000 patient days (95%CI)
All patients	16,338	4.263	467,812	9.1 (8.8 to 9.4)
					Mean difference (95%CI)	p
Medical patients
Sepsis	974	449	23,738	19 (17 to 21)	4.0 (1.8 to 6.4)	< 0.001
AECOPD	971	402	27,074	15 (13 to 16)	Reference
Other respiratory diseases	939	349	25,729	14 (12 to 15)	-1.3 (-3 to 0.8)	0.23
Vascular and infectious brain diseases	921	258	26,096	9.9 (8.7 to 11)	-4.9 (-6.8 to -3.0)	< 0.001
Urogenital diseases	949	224	27,674	8.1 (7.1 to 9.2)	-6.8 (-8.6 to -4.9)	< 0.001
Myocardial infarction	620	129	17,986	7.2 (6.0 to 8.5)	-7.7 (-9.6 to -5.7)	< 0.001
Other circulatory diseases	943	141	27,748	5.1 (4.3 to 6.0)	-9.8 (-11 to -8.0)	< 0.001
Gastrointestinal diseases	942	136	27,683	4.9 (4.1 to 5.8)	-9.9 (-12 to -8.2)	< 0.001
Endocrine diseases	972	116	29,070	4.0 (3.3 to 4.8)	-11 (-13 to -9.0)	< 0.001
Cancer and hematologic diseases	878	71	26,024	2.7 (2.2 to 3.5)	-12 (-14 to -11)	< 0.001
Surgical patients
Hip- and knee replacement surgery	951	329	27,820	12 (11 to 13)	4.3 (2.6 to 6.0)	< 0.001
Other orthopedic surgery	904	315	26,009	12 (11 to 14)	4.6 (2.8 to 6.3)	< 0.001
Other major abdominal surgery	652	200	17,241	11 (10 to 13)	4.0 (2.1 to 6.0)	< 0.001
Urogenital surgery	888	291	26,438	11 (9.8 to 12)	3.5 (1.8 to 5.1)	< 0.001
Heart surgery	511	146	14,789	9.9 (8.4 to 12)	2.3 (0.4 to 4.3)	0.01
Vascular surgery	854	240	24,904	9.6 (8.5 to 11)	2.1 (0.5 to 3.7)	0.01
Intracranial surgery	609	167	17,323	9.6 (8.2 to 11)	2.1 (0.3 to 3.9)	0.02
Colorectal resection	914	197	26,136	7.5 ( 6.5 to 8.7)	Reference
Gynecological surgery	915	103	27,420	3.8 (3.1 to 4.6)	-3.8 (-5.1 to -2.5)	< 0.001
Lung- and mediastinal surgery	31	0	900	0 (0 to 5.3)	-7.5 (-9.0 to 5.9)	0.01

Data is presented as mean estimates (95% confidence interval) and mean difference with 95% confidence intervals. P-values are as calculated with two sample proportions test. AECOPD, Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

Thirty percent of the patients with calls to EMS were readmitted within 30 days as compared with 7% of patients without EMS calls (p ≤ 0.001). Patients with EMS contact had an overall mortality of 26% whereas this rate was 10% in patients without EMS contact (p ≤ 0.001) (Table 3).

Table 3

– Readmission and mortality according to highest priority call
	Any EMS contact (n = 2678)	1-1-2 (n = 690)	1813 (n = 1,730)	Other EMS contact (n = 258)	No EMS contact (n = 13,660)	Odds Ratio [95%CI] Any EMS contact vs No EMS contact	p
30-day readmission	720 (30%)	252 (37%)	389 (23%)	74 (31%)	958 (7%)	4.76 [4.34 to 5.55]	< 0.001
30-day mortality	683 (26%)	210 (30%)	399 (23%)	74 (29%)	1,375 (10%)	3.03 [2.77 to 3.44]	< 0.001

30-day readmission rates and mortality among EMS callers based on highest priority call. CI: Confidence Interval. P-values are calculated with Fisher’s exact test.

This registry-based study found patients admitted due to AECOPD were only surpassed by post-sepsis discharge in frequency of post-discharge EMS calls among medical patient categories. No compelling trend was found within the surgical categories. The majority of EMS callers were readmitted within 30-days and our study indicated that the EMS callers had higher NEWS scores during their hospitalization. Mortality in one out of four patients with EMS contact underlines the severity of deterioration in these patients after hospital discharge. Emergency response A (urgent and lifethreatening cases) constituted 38.5% of ambulance responses for the cohort, closely aligning with the total number of ‘Response A’ ambulance dispatches for the entire region in 2020, which also includes accidents and other emergencies[12].

Our findings of a higher maximal NEWS in patients with post-discharge EMS contact contrasts with a recent study of 265 patients having continuous in-hospital monitoring after major abdominal surgery or during hospitalization due to AECOPD, which did not find association between number of sustained vital sign deviations prior to discharge and risk of readmission[22]. That study demonstrated that 77% of readmitted patients had SpO2 levels < 80% for at least 1 minute, while this deviation was also observed in 75% of patients who were not readmitted.

The majority of inquiries from patients after hospital admission for AECOPD were related to respiratory symptoms, indicating that certain patient groups seek EMS assistance for symptoms related to prior hospitalization. The findings of this study are in accordance with Søvsø et al.[15] who conducted a population-based historic cohort study of frequent ambulance users in Denmark, and found that symptoms with the most significant increase in frequency of ambulance use were breathing difficulties and chest pain. The study demonstrated that respiratory diseases were linked to 13% of frequent ambulance usage, with “other chronic obstructive pulmonary disease” accounting for 57% of the diagnoses. Similar findings were seen in a feasibility study of a social service intervention in frequent EMS users by Weiss et al.[23]. The study revealed that among 84 US EMS users, 25% of the calls were related to chronic respiratory disease. Christiansen et al.[24] investigated 148,757 patient’s diagnoses from hospital stay after calling 1-1-2, and concluded that non-specific diagnoses constituted a significant part of the hospital diagnoses for patients brought to hospital through EMS in the Northern Danish Region.

Several studies have found repeated calls to the EMS to be associated with psychiatric or alcohol related health problems[25]–[27]. Our study did not include such aspects, and it is worth recognizing that these factors could potentially affect our study results as confounding variables since reasons behind calls to EMS might be influenced by many complex reasons.

We have not identified studies that have investigated EMS utilization patterns following hospitalization and its potential correlation with hospital readmissions. Our study reveals a significant prevalence of post-discharge calls among major medical and surgical populations, while other categories of lower-risk hospital admissons may have fewer calls after discharge. Within our study, patients with EMS contact demonstrate higher probability of experiencing severe outcomes, as indicated by high readmission- and mortality rates. These findings underscore the importance of identifying frequent users to mitigate the risk of clinical deterioration in the post-discharge period.

Strengths and limitations

The strength of this study is the large and complete register-based dataset, offering a substantial and representative sample across a region with urban as well as rual areas. This allows study results to be generalizable to other regions with similar EMS setups and populations. The use of the CPR-system is a notable advantage, enabling cross-registry data acquisition and complete follow-up.

The study also presents some limitations. Firstly, we defined the study cohorts based on well-known categories, opting not to include certain perceived low-risk hospitalizations. Certain exposure groups include all discharged patients in the study period (owing to the constrained annual patient volume), while other groups (categories of frequent diagnoses such as AECOPD) were smaller subsets of the yearly cohort.

Secondly, we used two hospitalized groups as reference categories rather than using the general nonhospitalized population. Our study is therefore not able to compare the call patterns with the general population as this has been reported previously[15].

Thirdly, some discharged patiens may bypass EMS and contact the discharging hospital department directly or become readmitted to hospital through their general practitioner. Some specific cohorts might have the number of calls underestimated, but contact to the Emergency Helpline is mandatory outside office-hours, and our sample therefore represent those acute contacts occurring during evening- and night-time, weekends, and with emergency status. Not all EMS calls were registered with an organ system code, potentially leading to variations in rates for symptoms in calls. However, this data is missing at random and unrelated to specific SKS-groups. Our findings indicate that certain patient categories constitue a larger burden on the healthcare system in terms of multiple EMS calls after discharge. This identification may guide targeted interventions to reduce the risk of unobserved patient deterioration after discharge. Improving care coordination and communication between healthcare facilities and community services, along with implementing hospital-at-home monitoring, could be instrumental in achieving these objectives.In conclusion, patients discharged after hospitalization due to AECOPD and sepsis have a higher 30-day EMS call frequency compared with other medical cohorts, whereas a more even pattern was seen after discharge from various surgical admissions. These data may be used to prioritize resources for improved post-discharge outcomes.

Funding statement

A grant from The Danish Regions and The Danish Agency for Digitization supported this work.

Conflict of interest statement

CSM and EKA have created a start-up company, WARD247 ApS, with the aim of pursuing the regulatory and commercial activities of the WARD-project. WARD247 ApS has obtained license agreement for any WARD-project software and patents. One patent has been filed: “Wireless Assessment of Respiratory and circulatory Distress (WARD) – Clinical Support System (CSS) – an automated clinical support system to improve patient safety and outcomes.”

Acknowledgements

None.

Data availability statement

Original data will not be available.

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You are reading this latest preprint version

Frequency of Emergency Medical Service contacts after hospital admissions

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Study Design

Setting

Variables

Statistics

Results

Outcome data

Discussion

Strengths and limitations

Declarations

Funding statement

Acknowledgements

Data availability statement

References

Supplementary Files

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