Association between patient characteristics and recommendations by medical on-call service 116117 in Germany: a cross sectional observational study

doi:10.21203/rs.3.rs-5156612/v1

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Association between patient characteristics and recommendations by medical on-call service 116117 in Germany: a cross sectional observational study

https://doi.org/10.21203/rs.3.rs-5156612/v1

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Background:

Use of emergency departments has increased in recent years. Different efforts address this problem, eg, medical on-call services. The DEMAND intervention is based on a computer-assisted initial telephone assessment implemented at the regional associations of statutory health insurance physicians in Germany. In this intervention, recommendations for healthcare settings suited best to the patients’ health problems were given by telephone. This study aimed to analyse if patient characteristics are associated with the output of the intervention, ie, specific setting recommendations.

Methods:

This cross-sectional observational study based on a standardised postal survey of patients who received the intervention. Recommended and used settings, and data on sociodemography, health status at the time of the emergency call, past health service use, and health literacy were collected by patient self-report. The study population was characterised by descriptive statistics. For analysing the association between recommendations, and patient characteristics, multilevel, multivariable logistic regression models adjusted for random effects at the level of regions and months of observation within regions were conducted.

Results:

1,756 patients participated in the survey. The median age of the patients was 66 years, 59.0% were women and 30.2% living alone. Recommendations for emergency home visits were associated with worse self-rated health (OR 0.67, 95% confidence interval: 0.55/0.81, p<0.001). Telephone counselling was associated with lower age (0.71,0.59/0.85,p<0.001), lower subjective treatment urgency (0.65,0.51/0.82,p<0.001) and health problems not classified as symptoms and complaints (0.41,0.25/0.68,p=0.001) or infections (0.22,0.09/0.57,p=0.002.). Emergency departments were associated with better self-rated health (1.37,1.11/1.70,p=0.003) and health problems classified as injuries (3.12,1.67/5.83,p<0.001). Rescue service were associated with higher age (1.44,1.15/1.81,p=0.002) and a higher subjective treatment urgency (2.51,1.83/3.43,p<0.001). General practices were associated with lower subjective treatment urgency (0.58,0.44/0.76,p<0.001) and health problems not classified as injuries (0.26,0.10/0.68,p=0.006). Emergency practices were associated with lower age (0.60,0.48/0.74,p<0.001), and specialist practices were associated with health problems classified as symptoms or complaints (3.75,1.49/9.45,p=0.005).

Conclusions:

Most associations between patient characteristics and recommendations were comprehensible and in line with the aim of the intervention. No disparities in access to specific services were identified. However, it should be clarified why patients with better self-rated health were more likely to receive recommendations for emergency departments.

patient characteristics

recommendations by medical on-call service

specific setting recommendations

cross sectional observational study

In Germany and many other countries, the use of emergency departments has increased significantly in recent years, particularly by patients with less urgent needs [1–4]. A German study identified that more than half of the patients visiting an emergency department had low subjective treatment urgency. In addition to the urgency of the health problem, the decision to visit an emergency department can also be affected by perceived structural circumstances, eg, regarding availability of services, and individual patient preferences, eg, assumptions about the quality of diagnostic and treatment facilities [5].

There are different efforts for managing the increased emergency department utilisation, eg, medical on-call services that utilise computer-assisted telephone triage systems. These systems are designed to coordinate patient pathways within the healthcare system after evaluating the symptoms over phone [6]. However, evidence in this topic is limited and inconsistent. Therefore, further evaluations are required [7].

Few studies examined the associations between patient characteristics and recommendations by medical on-call services. Dale et al. reported an association of older age and higher rates of referral for home visits for a nursed-led out-of-hours telephone triage and advice service in general practice [8]. Another study found that older adults and patients with diabetes mellitus, dementia, or previous cerebral infarction were at risk of undertriage, ie, being recommended to visit a hospital within 1 hour and 6 hours [9].

In the German Health Care System, available healthcare settings for emergencies are rescue services (emergency treatment on site and transport to the hospital), emergency departments (ambulatory treatment at hospital), emergency home visits (visit by outpatient physicians), emergency practices (outpatient treatment outside office hours), telephone counselling (advice by physicians), specialist care (outpatient specialist care during office hours) and general practice (primary care during office hours). Patients are free to decide which emergency service they use. Outpatient emergency services are organised by the regional Associations of Statutory Health Insurance Physicians (ASHIPs) [10].

The DEMAND (“implementation of a standardized initial assessment as the basis of DEMAND management in outpatient emergency care”) intervention is one of various strategies [11–13] to improve patient allocation in out-of-hours care. The DEMAND intervention is implemented at the telephone services of ASHIPs. Core element of this intervention is the software SmED (“Strukturierte medizinische Ersteinschätzung in Deutschland”), which had been developed on the basis of the established software SMASS (“Swiss Medical Assessment System”) [14]. In this intervention, medical staff conducted computer-assisted structured initial assessments using SmED and gave recommendations for healthcare settings suited best to the patients’ health problems.

This study aimed to analyse if individual patient characteristics are associated the output of the DEMAND intervention, ie, with specific setting recommendations.

This cross-sectional observational study based on a standardised postal survey of patients who had received the DEMAND intervention. The intervention is described in detail in another paper [10]. In short, at the medical on call service 116117, trained medical stuff conducted computer-assisted structured initial assessment with the patient via telephone and gave recommendations of suitable treatment settings after appraisal of the specific situation of the patient. The DEMAND intervention was evaluated in the telephone services of eight ASHIPs located in the German federal states of Bavaria, Brandenburg, Bremen, Hesse, North Rhine-Westphalia (represented by two ASHIPs in the regions North Rhine and Westphalia-Lippe), Schleswig-Holstein and Thuringia. Compliance and patient satisfaction with the intervention have been described elsewhere [10]. DEMAND has also been implemented in selected hospitals, which has been analysed in another study [15].

Selection of participants and data collection

The selection of participants and data collection have been described in detail in a previous publication of Schäfer et al. [10]. In short, due to a strict approval process for transferring personally identifying information by the German federal governments, the start of data collection was different between regions. In North Rhine-Westphalia and Bremen, the observation time was between January and December 2020, in Brandenburg between April 2020 and March 2021, in Bavaria between June 2020 and February 2021, in Hesse between July 2020 and March 2021, in Thuringia between October 2020 and March 2021 and in Schleswig-Holstein between January and March 2021.

The respective ASHIP produced list of patients who took part in the intervention after each finished month of the observation time. Exclusion criteria were age less than 18 years or no reported valid postal address. The study centre received the contact data by electronic data transmission. A total of 10 000 patients were randomly selected. Questionnaire and patient information were sent by mail to the selected patients. The patients gave their consent to study participation by returning the completed questionnaires to the study centre. Patients were excluded retrospectively, if the letter could not be delivered due to an incorrect postal address, if the patients reported that they did not use the telephone service of the respective ASHIP, and if relatives or other household members reported that the patients were not able to fill out the questionnaire (eg, due to functional limitations) or that the patients had died.

Measurements

Recommended and used settings of emergency care were collected by patient self-report. Multiple answers were possible. Patients’ satisfaction with the computer-assisted initial assessment and the used setting were evaluated by rating their agreement to eight statements on a four point Likert scale.

Data on sociodemography, health status at the time of the emergency call, past health service use, and health literacy were also collected. Sociodemographic data involved age, sex, living arrangement, educational level of the patients, and country of birth of the study participants and their parents. The educational level was coded according to the CASMIN classification [16] in three hierarchical categories. Self-rated health, depressiveness, subjective treatment urgency, and reasons for consulting the telephone services of the ASHIP were assigned to health status. A visual analogue scale ranging from 0 (indicating the worst) to 100 (indicating the best imaginable health status at the day of the initial assessment) was used for assessing self-rated health.

Patients rated their subjective treatment urgency on a numerical rating scale ranging from 0 (indicating no urgent need for treatment) to 10 (indicating very urgent need for treatment and/or life threatening condition). The Patient Health Questionnaire PHQ-2 [17] was used to screen for symptoms of depression in the two weeks before initial assessment. It consists of two items and results in a summary score ranging between 0 (indicating no symptoms) and 8 (indicating both symptoms occurring almost every day). Patients reported their consultations reasons by open questions. JHO and AM coded these information retrospectively in the International Classification of Primary Care, Second Revision (ICPC-2) [18], which facilitates grouping by organ system and diagnosis type (eg, “symptoms/complaints”, or “infections”). The past health service use in the three months before the initial assessment (general practices, specialist practices, hospitals, and emergency services) was also collected by patient report. To collect health literacy we used the European Health Literacy Questionnaire HLS-EU-Q16 rated on a four point Likert scale. For our analyses, we dichotomised the data. The summary score was divided in three hierarchical categories: inadequate (0–8 points), problematic (9–12 points) and sufficient (13–16 points) [19, 20].

Statistical analyses

The study population was characterised by descriptive statistics. For analysing the association between recommendations, and patient characteristics we conducted multilevel, multivariable logistic regression models adjusted for random effects at the level of regions and months of observation within regions. Independent variables included sociodemographic data and the variables describing health status, past health service use, and health literacy. The respective setting recommendations were defined as dependent variables, which resulted in seven statistical test. In order to correct the Alpha-level for multiple testing, we performed Bonferroni-adjustment for these analyses and defined an Alpha-level of p ≤ 0.007 as statistically significant. All statistical analyses were based on the available data and performed using Stata 15.1.

Characteristics of the study population

The recruitment process and the study population had been described elsewhere in detail [10]. Briefly summarized, 408,781 patients were checked for eligibility. 67,355 patients had to be excluded. Out of the remaining 341,426 eligible patients, we randomly selected and contacted 10,000 patients. 527 had to be retrospectively excluded due to exclusion criteria or because they died or had functional limitations. In the end, 1,756 patients participated in the survey (18.5 % rsponse rate).

The median age of the patients was 66 years (interquartile range: 50–79), 59.0% were women and 30.2% were living alone. 23.0% of the patients had a tertiary educational level, 43.4% had a secondary educational level and 33.7% had uncompleted education or primary educational level. The majority of the patients was born in Germany (85.9%), and few patients were born abroad (8.2%) or had at least one parent born abroad (5.9%). The median subjective treatment urgency was 7 (5–8). The median of self-rated health on the day of the intervention was 40 (24–60) on a scale ranging 0 to 100. 41.4% of the patients had a sufficient health literacy, 36.2% problematic and 22.4% inadequate. 68.7% of the patients reported symptoms or complaints, 6.9% injuries, 5.5% infections and 19.6% other diagnoses as health problems.

Association between recommendations and patient characteristics

The recommended settings after computer-assisted structured initial assessment are presented in Table 1. For example, 40.1% of the patients received a recommendation for getting emergency home visits, and 4.5% for visiting a specialist practices. The distributions of sociodemographic and health-related data by recommended settings are shown in Tables 2a and 2b and the associations between these data and the setting recommendations are shown in Tables 3a to 3d.

In short, recommendations for

emergency home visits were associated with worse self-rated health (odds ratio 0.67 for 30 points difference, 95% confidence interval: 0.55/0.81, p < 0.001),
telephone counselling was associated with lower age (0.71 for 20 years difference, 0.59/0.85, p < 0.001), lower subjective treatment urgency (0.65 for 3 points difference, 0.51/0.82, p < 0.001) and health problems not classified as symptoms and complaints (0.41, 0.25/0.68, p = 0.001) or infections (0.22, 0.09/0.57, p = 0.002),
emergency departments were associated with better self-rated health (1.37, 1.11/1.70, p = 0.003) and health problems classified as injuries (3.12, 1.67/5.83, p < 0.001),
rescue service were associated with higher age (1.44, 1.15/1.81, p = 0.002) and a higher subjective treatment urgency (2.51, 1.83/3.43, p < 0.001),
general practices were associated with lower subjective treatment urgency (0.58, 0.44/0.76, p < 0.001) and health problems not classified as injuries (0.26, 0.10/0.68, p = 0.006),
emergency practices were associated with lower age (0.60, 0.48/0.74, p < 0.001), and
specialist practices were associated with health problems classified as symptoms or complaints (3.75, 1.49/9.45, p = 0.005).

There were no statistically significant associations of setting recommendations to other sociodemographic data (including sex) or indicators of health status, healthcare utilisation or health literacy.

The aim of our study was to identify associations between patient characteristics and recommendations for the different treatment settings. Most findings indicate that the settings are frequently recommended due to their specific function within the healthcare system. For example, patients with higher treatment urgency were visited by the rescue service, patients with low self-rated health received home visits by outpatient physicians, patients with health problems classified as symptoms or complaints were sent to outpatient specialist care and patients with low treatment urgency should visit general practice. However, patients with better self-rated health had a higher likelihood of being advised to visit the emergency department, which might point to recommendations based on patient preferences rather than medical needs. In this context it needs to be mentioned that our study does not inform about the appropriateness of given recommendations.

There were other studies on the appropriateness of computer-assisted initial assessments. SMASS was analysed in a prospective surveillance study in 2012. Decisions of nurses assisted by a computerised decision support system were compared to retrospective assessments of hospital physicians and primary care physicians. There was low agreement between the assessments of the three groups. Seven of the 153 examined cases appeared to be under-triaged and a risk to health or life was identified in one case. The authors concluded that computer-assisted telephone triage was safe, but required competent specialists with dedicated training in communication [21]. An evaluation of nurse telephone consultations aided by a computer-based call management system in 1998 reported that overall workload of general practitioners was reduced by half and faster access to advice was facilitated. Moreover, there was no increase in the number of adverse events [22]. A 2007 study from Sweden on computer-supported telephone nurse triage reported that 97.6% of 362 participants were referred to the appropriate level of care [23].

Only a small number of studies took the associations between patient characteristics and recommendations by medical on-call services into account. For example, similar to our results, older age was also associated with higher rates of referral for home visits in a nursed-led out-of-hours telephone triage and advice service in general practice [8]. In another study, older adults and patients with diabetes mellitus, dementia, or previous cerebral infarction were at risk of undertriage [9]. Moreover, individuals living in rural areas had fundamentally different pattern of service use than individuals living in urban areas. Rural callers were more likely to use family physicians and less likely to use emergency departments after being advised to use the respective service [24]. Nakubulwa et al. described a better compliance in following the advice given by a non-emergency medical helpline for children aged less than 16 years, women, and individuals of Asian/Asian British ethnicity [25].

Strengths and limitations

Our study has a comparably low participation rate of 18.5% which could be a consequence of using contact data of patients, which were collected for healthcare and not for study purposes. Many exclusion criteria could not be assessed systematically. It is possible that some patients died without our knowledge or could not respond because of bad health condition or functional limitations. It is also possible that some patients gave wrong contact data. If patients lived in a hotel or holiday apartment or were at a friend’s place while utilising the telephone services they could possibly not be reached for our study. Nevertheless, a low participation rate may also reflect unwillingness to participate in the study.

We have no knowledge of whether the study participants who received the intervention had a better or worse adherence and satisfaction to the computer-assisted structured initial assessment compared to patients who did not receive the intervention. This is because of the legal regulations requiring initial assessment in the telephone services of ASHIPs from 1 January 2020. Therefore, it was not possible to establish a control group of the intervention.

The items measuring patient satisfaction were not validated and therefore reliability and validity of this instrument are unknown. In other cases, we used validated and established instruments like PHQ-2 [17] and HLS-Q16-EU [20]. The answers of the patients might have been affected by recall bias due to the fact that the questionnaire was sent out between 4 and 72 days after receiving the intervention. Also errors and social desirability might have affected the data quality. In contrast to these limitations, it is worth emphasizing that the large sample size and multilevel, multivariable methods allowed a detailed analysis.

The DEMAND intervention aimed to improve patient allocation in emergency care by computer-assisted structured initial assessment. Most associations between specific recommendations and patient characteristics were comprehensible and in line with the aim of the intervention. No disparities in access to specific services were identified. However, it should be clarified why patients with better self-rated health were more likely to receive recommendations for emergency departments.

Ethics approval: The study was approved by the Ethics Committee of the Hamburg Medical Association on 08 April 2019 (approval no. PV6035).

Consent for publication: Not required.

Availability of data and materials: De-identified data are available upon reasonable request from the corresponding author.

Competing interests: The authors declare that no competing interests exist.

Funding: This work was supported by the Innovation Fund of the Federal Joint Committee (grant no. 01VSD17019).

Authors' contributions: IS, AM, JO, DL and MS conceived and designed the study. IS performed the statistical analyses. HH drafted the manuscript. All authors commented on the draft and read and approved the final version of the manuscript.

Acknowledgements:

We would like to express our appreciation to the staff members of the intervention sites who facilitated this study with their participation and to the Associations of Statutory Health Insurance Physicians of Bavaria, Brandenburg, Bremen, Hesse, North Rhine, Westphalia-Lippe, Schleswig-Holstein, and Thuringia for their support.

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Tables 1 to 3 are available in the Supplementary Files section.

No competing interests reported.

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You are reading this latest preprint version

Association between patient characteristics and recommendations by medical on-call service 116117 in Germany: a cross sectional observational study

Status:

Version 1

Abstract

Background

Methods

Selection of participants and data collection

Measurements

Statistical analyses

Results

Characteristics of the study population

Association between recommendations and patient characteristics

Discussion

Strengths and limitations

Conclusions

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1