Original Articles
Agustin Matias Muñoz a; Eliana Ludmila Frutos b; Ana Soledad Pedretti a c; Javier Alberto Pollan a; Daniel R Luna b; Bernardo Julio Martínez a c d; María Florencia Grande Ratti a c d
a Department of Internal Medicine, Hospital Italiano de Buenos Aires, Ciudad de Buenos Aires, Argentina.
b Department of Health Informatics, Hospital Italiano de Buenos Aires, Ciudad de Buenos Aires, Argentina.
c Emergency Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
d Internal Medicine Research Unit, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
Hospital Italiano de Buenos Aires is a high complexity healthcare center with electronic clinical records, which includes documents with data, assessments and varied information on the clinical state and development of patients during treatment. In 2011, based on a commercial model offering more security to patients, a multiple order computer system consisting of a predefined form containing a lising of several items, including studies and treatments, which facilitates selecting multiple orders, grouped based on a specific condition. Despite its numerous advantages and benefits, its use could bear unexpected consequences, like overprescription and increased costs.
KEY CONCEPTS:
Background
The medical order computer system is an informatic tool that allows patients to order medical studies from their clinical history. A specific type, termed "multiple order”, consists of a predefined form containing a listing of several items, and it has shown to have several advantages, and faclites choosing multiple orders grouped based on specific conditions. However, its use could have unexpected consequences over healthcare costs.
Contributions
Emergency healtchare centers face significant challenges when offering timely and high quality attention in an increasingly demanding scenario, with more patients and limited resources. In this scenario, work conditions (such as time and patient loads) could provide incentives for the acritical request of diagnostic tests, losing sight of clinical criteria. This work aims to compare the number of requested tests and their associated costs before and after disabling a multiple order system .
Abstract
Introduction: The computerized provider order entry (CPOE) is a computer-based tool with the potential to cause unintended adverse consequences despite its myriad benefits. We aimed to explore the effect of its inactivation on requests for complementary studies and the associated costs. Methods. Cross sectional study at the Emergency Department of Hospital Italiano de Buenos Aires, which included a consecutive sample of pre-intervention (January-February 2020) and post-intervention (2021) consultations. Using secondary bases, the variables included were administrative debits and their respective billing prices. Results. There were 27,671 consultations in 2020 with a total median value of $474, and 20,819 with $1,639 in 2021. After the analysis restricted to the area of moderately complex clinics (excluding COVID-19 consultations), the following was found: a decrease in the median number of practices per consultation (median of 11 vs. 10, p=0.001), a decrease in the request for at least one laboratory practice (45% vs. 39%, p=0.001), without finding significant changes in global costs (median $1,419 vs. $1,081; p=0.122) or in specific laboratory costs (median $1,071 vs. $1,089, p=0.710). Conclusion. Despite inflation, a significant reduction in the number of practices was achieved and overall costs per consultation were maintained. These findings demonstrate the effectiveness of the intervention, but an educational intervention aimed at reminding the potential harm of overuse and the health costs of unnecessary studies will be necessary.
Key words: medical order entry systems; medical informatics; health care costs; emergency service hospital.
Abstract
Introducción: La plantilla de órdenes múltiples es una herramienta informática que podría producir consecuencias inadvertidas pese a sus innumerables beneficios. Nos propusimos explorar el efecto de su inactivación sobre las solicitudes de estudios complementarios y los costos asociados. Métodos. Corte transversal en la Central de Emergencias de Adultos del Hospital Italiano de Buenos Aires, que incluyó muestra consecutiva de consultas pre-intervención (Enero-Febrero 2020) y post-intervención (2021). Mediante el uso de bases secundarias, las variables incluidas fueron los débitos administrativos y sus respectivos precios de facturación. Resultados. Hubo 27.671 consultas en 2020 con una mediana de valor total de 474$, y 20.819 con 1.639$ en 2021. Tras el análisis restringido al área de consultorios de moderada complejidad (excluyendo consultas por COVID-19), se encontró: una disminución en la mediana del número de prácticas por consulta (mediana de 11 vs 10, p=0,001), una disminución en la solicitud de al menos una práctica de laboratorio (45% versus 39%, p=0,001), sin encontrar cambios significativos en costos globales (mediana 1.419$ vs 1.081$; p=0,122) ni en costos específicos de laboratorio (mediana 1.071$ vs 1.089$, p=0,710). Conclusión. Pese a la inflación interanual, se logró una reducción significativa en el número de prácticas y se mantuvieron los costos globales por consulta. Estos hallazgos demuestran la efectividad de la intervención, pero serán necesarias medidas educativas que apunten al recordatorio de los potenciales daños en la sobreutilización, y los costos sanitarios de los estudios innecesarios.
Palabras clave: sistemas de entrada de órdenes médicas; informática médica; costos de la atención en salud; servicio de urgencia en hospital.
Resumo
Introdução: A entrada computadorizada de pedidos de fornecedores (CPOE) é uma ferramenta de computação que pode levar a consequências não intencionais, apesar de seus inúmeros benefícios. Procurou-se explorar o efeito de sua inativação nas solicitações de estudos complementares e os custos associados. Métodos. Estudo transversal no Serviço de Emergência do Hospital Italiano de Buenos Aires, que incluiu uma amostra consecutiva de consultas pré-intervenção (janeiro-fevereiro 2020) e pós-intervenção (2021). Utilizando bases secundárias, as variáveis incluídas foram débitos administrativos e seus respectivos preços de faturamento. Resultados. Foram 27.671 consultas em 2020 com valor médio total de $ 474, e 20.819 com $ 1.639 em 2021. Após a análise restrita à área de clínicas de complexidade moderada (excluindo consultas de COVID-19), foi constatado o seguinte: o número mediano de consultórios por consulta (mediana de 11 vs. 10, p=0,001), uma diminuição na solicitação de pelo menos um consultório de laboratório (45% vs. 39%, p=0,001), sem encontrar mudanças significativas no custos (mediana $ 1.419 vs. $ 1.081; p=0,122) ou em custos laboratoriais específicos (mediana $ 1.071 vs. $ 1.089, p=0,710). Conclusão. Apesar da inflação homóloga, conseguiu-se uma redução significativa do número de consultórios e mantiveram-se os custos globais por consulta. Esses achados demonstram a eficácia da intervenção, mas será necessária uma intervenção educativa destinada a lembrar os danos potenciais do uso excessivo e os custos de saúde de estudos desnecessários.
Palavras chave: sistemas de registro de ordens médicas; informática médica; custos de cuidados de saúde; serviço hospitalar de emergência.
The computerized physician order entry (CPOE) is an informatic tool that allows patients to request studies, checkups, procedures, medicaton, and other types of non-pharmacological treatments from their electronic clinical records.
Historically, it was introduced into healthcare systems based on a commercial system with greater safety. Its implementation in drug prescription has been shown to reduce medical errors and adverse effects [1]. However, the impact on imaging studies and laboratory tests has not received much attention.
Several studies have demonstrated the benefits of a specific type of CPOE termed “Multiple Order”, which groups related practices based on a given condition, disease or procedure that allow for the selection of multiple options on a predefined interface, based on criteria defined by users. These have th potential to improve efficiency in attention [2], and to reduce the time between issuing requests and obtaining results [3]. However, some reports have revealed these systems could have undesired consequences [4]. A local study from 2004 showed higher rates of lactic acid and erythrocyte sedimentation rate laboratory requests in hospitalized patients, when these practices were included in the multiple order system [5]. Evidence sows there is a consistent tendency to the excessive and innecesary use of resources and the of costs around health services [6].
On the other hand, emergency services face significant challenges when striving to deliver timely and high quality services in a scenario in which there is a growing number of patients and there are limited resources [7]. In this context, healthcare providers invest in CPOE systems expecting they will improve wait times for complementary test results, increasing the speed of diagnostic and therapeutic decision making, and reducing service times [8].
In our hospital, CPOEs are used since 2011, and due to the issues introduced above, and taking into account potential unexpected effects, in December 2020 we decided to implement an intervention, and inactivate multiple requests. The goal was to assess the effect of this intervention on the number of requested practices, and exploring healtchare costs before and after the intervention.
Transversal cohort of the emergency central for adults of Hospital Italiano de Buenos Aires. This high complexity center (Plan de Salud) provides a prepaid health insurance card and has 180.907 affiliates as of March 2021, and the adult emergency service provides 550 daily appointments on average, and is divided into four areas, based on patient complexity, which is determined based on the patient's condition upon entry.
Patients who arrive at the emergency central (whether by feet, wheelchair or ambulance) are received by nursing staff trained on triage, who conduct a brief interview and assess the patient's state. Triage aims to determine: I. reasons for seeking medical attention; II. whether the patient requires immediate intervention (yes/no); III. whether the patient is at risk (yes/no); IV. if the patient will require at least two resources (such as placing an intravenous line, providing oxygen therapy, or carrying out complementary tests); V. whether vital signs are normal (yes/no). Using these data, patients are assigned to an emergency category (4 existing colors, ranked from lower to higher priority: white, green, yellow and red) according to their condition, and are assigned to one of four of the available areas: critical care (Area A), intermediate care (Area B), moderate complexity (C), spontaneous demand or low complexity (Area D).
Intervention
Our intervention started on December 2020, and consisted of the inactivation of multiple orders in our
CPOE.
Requests were grouped in a predefined template, based on a specific disease, and were presented as a multiple choice listing from which individual items could be chosen based on the criteria of practitioners themselves .
In intensive care and medical wards, the most frequently used itemset was the intensive care unit for adults, (see Figure 1), which grouped related requests, thus accelerating manual search, and made it possible to select individual items.
Figure 1. Screen capture of the multiple-order system showing a specific itemset, the adult intensive care unit routine.
Participants and Sample Size
Consecutive samples taken from the study period: before intervención (Enero-Febrero 2020) and after intervention (from the same epidemiological weeks in 2021). Non probabilistic sampling was based on the feasibility and convenience of carrying out this exploratory research in the context of the workflow changes implemented by hospital management at a given time.
We used reliable, high-quality secondary sources for collecting data. On one hand, we used the CEA control board, which contains administrative variables related to appointments (examples: area where medical care was provided, type of health insurance, checkup duration, patient state at discharge, among others). Additionally, we requested that management made hospital data avaiable for research, and management granted us access to a list of all available services. Such listings are regularly used reference tools, and are essential for everyone involved in healthcare management, specifically for those in charge of billing medical services. They list code-indexed medical services (such as laboratory or imaging studies, or drug delivery), with their respective costs or pricing, in ARS. This was made possible by the data capture implemented by the Integrated Clinical Data system packed with the traditional electronic records, which stores mirrored databases with anonymized data (which is both private and confidential).
Firstly, we carried out a descriptive analysis of all appointments made in our four areas, reporting: the total number of appointments, the number of patients (since in a given timestamp the same patient can seek medical care more than once), state at discharge, and total spending (the aggregated cost of all services provided during an appointment). In our analysis, we included the most taxing services (defined as the set of medical services with the highest prices) and the most requested studies (defined by volume or frequency of request, independently from their unit prices).
Secondly, we carried out a descriptive analysis of all appointments restricted to low complexity areas (C and D), which generally consist of patients with common primary healthcare needs, and account for the bulk of patient volume. Normally, practitioners delivering medical services in these areas are specialized or general clinicians, who do not require cross-consultation involving other doctors with different specialties.
Third, we used sensitivity analysis, aiming to identify healthcare costs specific to the pandemic that could distort data in our study period. The analysis consisted in a before-after comparison, restricted to appointments made in area C and excluding all those involving swabbing (CORONAVIRUS COVID-19 RNA). We used chi-squared for categorical variables, and Mann-Whitney for numerical variables. We considered p-values significant when p<0,05. Our protocol was approved by the institutional ethics comittee (CEPI#5960) and used STATA 17.0 for analysing data.
During our study period, median global prices for a checkup in a ward went from $474 in January-February 2020 to $1639 in January-February 2021, which amounts to a more than threefold increase (Table 1).
Laboratory requests, or complementary studies such as imaging studies account for more than 70% of the total cost of a checkup.
The most costly services were: complementary studies like Computer Tomography (25.643,85$ in January 2020), and/or pharmacological services like fibrinolytics ($40271,70 in February 2020); which seem to be unavoidable based on the severity of the acute clinical conditions expected in shock room patients (area A). Something similar occured in 2021, with the extraction of foreign bodies by endoscopy and human fibrinogen having the highest cost.
As it can be seen in Table 1, in 2020 area D provided attention to most patients (50.7%), probably as a consequence of the logistic reestructuring directed at handling patients suspected of or diagnosed with COVID [9]. In both cases, less than 10% of appointments resulted in unexpected hospitalizations (7,.3% and 8,.4%, respectively).
As regards frequency, in 2020 the most requested items were: blood counts, serum creatinine, platelet counts, uremia, ionograms, hepatograms, hepatograma, blood sugar, basic coagulation tests, ph tests, lactic acid, complete urine tests,
urine culture, and abdominal ultrasound. During 2021 the observed frequencies were similar, but swabbing (CORONAVIRUS COVID-19 RNA) became common due to the pandemic.
Restricted analysis in areas C and D
We observed an increase in median global prices for checkups (from $507 in 2020 to $2.395 in 2021), despite decreases in patient volume (with a relative number of 19.439 in 2020 to 16.022 in 2021) [9].
Simoltaneously, the percentage of checkups with at least one laboratory study request increased (from 26.98% to 54.73%). However, it is noteworthy that in area D this number did not vary as much (from 19% to 23%), while in area C it increased considerably (from 45% to 77%). This allowed us to think in the pandemic phenomenon during 2021 and as such it was observed in Figure 2, interanual average prices were relatively stable when stratifying to separate checkups related and unrelated to COVID (defined as including or not including swabbing) in 2021.
Area C Sensitivity Analysis excluding COVID.
Figure 2. Average price of checkups in areas C and D, stratified by COVID.
We included all checkups conducted in area C during 2020 (n: 5393) and all checkups not related to COVID in 2020 (n: 3479) (Table 2). As regards primary outcomes, we observed a significant reduction in the percentage of checkups with at least one laboratory request (from 45% to 39%; p=0,.001), and a reduction in the total number of laboratory studies requested per checkup (median 11 to 10 items; p=0.001).
As regards secondary outcomes, we found a significant reduction in the percentage of patients requiring more than one blood test (de 24% a 19%; p=0.001), and a significant reduction in checkup duration (from 159 minutes to 134 minutes; p=0.001).
Pre- and post- deactivation of a specific itemset
The most commonly used itemset (intensive care unit for adults) contains 14 laboratory tests. As (Table 3) shows, the frequency of 10 items decreased significantly after interventions (with p-values below 0.001, p<0.001). It is worth mentioning the list of items whose frequency reduced significantly: ionogram (from 90.03% in 2020 to 82.69%; p=0.001), blood sugar (from 81.22% to 52,.56%; p=0.001) and blood ph (from 51.65% to 27.56%; p<0.001). This analysis was restricted to patients having a healthcare insurence in order to explore changes in total costs between periods, with a 17.80% inflation.
Whole itemsets were never requested, both before and after the intervention, with total leucocytes and partial oxygen pressure being the less frequently requested services (<1%).
Effect on other laboratory practices
We selected 10 laboratory practices of clinical interest. As Table 4 shows, a significant reduction (p<0,.05) was observed in five items: coagulogram, blood magnesium, blood calcium, ionic calcium and prothrombin time tests, while erythrocyte sedimentation rates increased significantly (from 14.10% to 17.56%, p=0.028)
|
Table 1. Total costs |
||
|
|
January-February 2020 |
January-February 2021 |
|
Total Number of Checkups |
27.671 |
20.819 |
|
Healthcare Plan |
67.33% (18631) |
69.54% (14478) |
|
Number of Patients |
20863 |
16583 |
|
First access area |
|
|
|
Condition at discharge |
|
|
|
Total checkup cost, amount $ * |
474 (1024) |
1.639 (2.301) |
|
RaCe: quick access to internal medicine specialists (e.g.: oncology actives) |
||
|
Table 2. Area C (sin COVID) |
|||
|
|
Before intervention |
After Intervention |
p value |
|
BASELINE |
|||
|
Age, in years * |
61 (39-76) |
56 (38-73) |
0.001 |
|
Sex (female patients) |
57.63% (3108) |
55.45% (1929) |
0.043 |
|
Health Insurance |
59.08% (3186) |
62.12% (2161) |
0.004 |
|
Hospitalization |
14.67% (781) |
14.29% (497) |
0,619 |
|
PRIMARY OUTCOMES (indirect cost estimation) |
|||
|
Total Cost for an appointment, amount $ * |
1419 (451-3428) |
1081 (484-3196) |
0,122 |
|
Laboratory (at least 1 practice) |
45.71% (2465) |
39.81% (1385) |
0.001 |
|
Number of lab practices * |
11 (8-13) |
10 (8-13) |
0.001 |
|
Laboratory Costs, amount $ (at least 1 service) * |
1071 (784-1985) |
1089.5 (722.5-966) |
0,711 |
|
Imagining (at least 1 service) |
37.33% (2,013) |
35.50% (1,235) |
0.081 |
|
Number of images * |
2 (1-3) |
1 (1-2) |
0.001 |
|
X-Rays (dychotomic) |
20.03% (1,080) |
20.87% (726) |
0.336 |
|
SECONDARY OUTCOMES (as a proxy of patient safety) |
|||
|
Checkup duration, in minutes * |
159 (79-276) |
134 (38-259) |
0.001 |
|
Fugue (LWBS) |
2.97% (160) |
6.32% (220) |
0.001 |
|
% with more than 1 extraction |
24.26% (598) |
19.13% (265) |
0.001 |
|
Death |
0% (0) |
0% (0) |
N/A |
|
* Median (pc25-pc75) |
|||
|
Table 3. Effect of the deactivation of the adult intensive care unit for adults itemset |
||||||
|
|
Before (2020) |
After (2021) |
p value |
Price |
Price |
Inflation |
|
At least 1 laboratory service |
45.63% (1454) |
37.95% (820) |
0.001 |
N/A |
N/A |
N/A |
|
KPTT |
3.16% (46/1454) |
2.68% (22/820) |
0.518 |
49.65 |
58.49 |
17.80% |
|
EAB |
51.65% (751/1454) |
27.56% (226/820) |
0.001 |
120,17 |
141.58 |
17.80% |
|
Serum creatinine |
92.50% (1345/1454) |
91.34% (749/820) |
0.324 |
45.6 |
53.72 |
17.80% |
|
Blood Sugar |
81.22% (1181/1454) |
52.56% (431/820) |
0.001 |
26.21 |
30.88 |
17.80% |
|
Hematocrit |
3.99% (58) |
0.85% (7) |
0.001 |
23.44 |
27.61 |
17.79% |
|
Hemogram |
92.03% (1338) |
94.02% (771) |
0.077 |
43.68 |
51.46 |
17.81% |
|
Hepatogram |
79.78% (1160) |
67.32% (552) |
0.001 |
197.52 |
232.69 |
17.80% |
|
Ionogram |
90.03% (1309) |
82.69% (678) |
0.001 |
98.44 |
115.98 |
17.81% |
|
Lactic |
35.35% (514) |
17.07% (140) |
0.001 |
39.42 |
46.44 |
17.81% |
|
Total Leucocytes |
0.48% (7) |
0% (0) |
0.047 |
37.94 |
44.69 |
17.79% |
|
Blood Po2 |
0.83% (12) |
0% (0) |
0.009 |
135.3 |
159.39 |
17.80% |
|
Platelets |
85.21% (1239) |
75.49% (619) |
0.001 |
23.44 |
27.61 |
17.79% |
|
TP |
8.46% (123) |
0.98% (8) |
0.001 |
46.88 |
55.23 |
17.81% |
|
Uremia |
90.99% (1323) |
90.61% (743) |
0.762 |
30.26 |
35.65 |
17.81% |
|
Complete itemset |
0% |
0% |
N/A |
917.95 |
1081.42 |
17.81% |
|
KPTT: Kaolin-activated Partial Tromboplastin time |
||||||
|
EAB: base acidic state |
||||||
|
Po2: partial oxygen pressure (arterial samplel) |
||||||
|
TP: prothrombin time |
||||||
|
Table 4. Effect of Health Insurance on other practices |
||||||
|
|
Before (2020) |
After (2021) |
p value |
Price |
Price |
Inflation |
|
Blood clotting |
44.77% (651) |
18.90% (155) |
0.001 |
78.63 |
92.63 |
17.80% |
|
Blood Magnesium |
26.48% (385) |
12.56% (103) |
0.001 |
41.39 |
46.44 |
12.21% |
|
Calcium |
20.84% (303) |
10.37% (85) |
0.001 |
38.48 |
43.18 |
12.21% |
|
VSG |
14.10% (205) |
17.56% (144) |
0.028 |
24,61 |
27.61 |
p12.20% |
|
Amylasemia |
13.14% (191) |
10.98% (90) |
0.133 |
39.82 |
44.68 |
p12.20% |
|
Prothrombin Time Tests |
7.91% (115) |
4.39% (36) |
0.001 |
142.07 |
159.41 |
p12.20% |
|
Ionic Calcium |
5.85% (85) |
3.90% (32) |
0.044 |
39.82 |
44.68 |
13.74% |
|
LDH |
4.88% (71) |
5.85% (48) |
0.318 |
80.32 |
90.12 |
12.21% |
|
BNP |
3.99% (58) |
5.00% (41) |
0.257 |
3484.81 |
4105.45 |
17.80% |
|
Sodium |
0.14% (2) |
0.37% (3) |
0.264 |
49.22 |
55.23 |
12.21% |
|
Erythrocyte sedimentation rate |
||||||
|
RIN: international normalization ratio |
||||||
|
LDH: dehydrogenase lactate |
||||||
|
BNP: natiruretic peptides |
||||||
|
Price, in ARS ($) |
||||||
Our findings show the number of appointments during which at least one laboratory study was requested decreased (45.71% versus 39.81%, p=0.001), as well as a reduction in the median number of practices per appointment (11 versus 10, p=0.001), without finding significant changes in costs after deactivating multiple order itemsets. However, we believe the percentage of patients with at least one laboratory study request is not the effect of laboratory interventions, but is related mostly to the clinical context of checkups (i.e. to variables intrinsic to patients, their reason for seeking medical attention and acute conditions). In contrast, the reduction in the number of laboratory studies requested is associated to interventions themselves, resulting in findings similar to prior works in Argentina [5], although the cited work focuses on a different kind of healthcare (hospitalized patients).
We detected a consistent and statistically significant decrease in the number of requests including 10 items (out of a total 14) after the intervention (some examples: blood ph, lactic acid, blood sugar and platelets) in the adult intensive care unit (the most used in healthcare practice) via sensitivity analysis focusing on multiple order. In this case analysis, we restricted our sample to appointments made by patients with a health insurance, since they include cost data, allowing us to analyze changes due to inflation in our study period, which was around 18% year-to-year (between 2020 and 2021).
During the last years the safety and sustainability of healthcare interventions has been called into question, and it is considered that the efficacy of around 50% of interventions in not known, little above a third are probably effective, and 15% are likely hazardous or provide no tangible benefits [10–12]. In such circumnstances, the existence of diagnostic and therapeutic interventions that are termed "low value" or "medical excesses" are defined as those providing little to no benefit to patients, have the potential to cause damage and incur in unnecessary costs, wasting resources from the limited pool available to healthcare systems [10, 13]. These "low value" interventions are considered different from “medical errors” or “mala praxis”, given they stem from expert recommendations, clinical practice guidelines and/or public policy [10,14]. Even so, "defensive medicine” (a practice destined to reduce the risk of lawsuit due to mala praxis) contributes to part of the increase in low value healthcare [10,15]. In this context, we evaluated 10 laboratory items which we consider must be requested only in the presence of specific clinical signs, such as blood calcium for acute delirium or blood magnesium for seizure, which during 2020 were requested with higher frequency (26.48% and 20.84% respectively). In our before-after comparison, five show a a significant decrease, and the only item increasing in frequency was erythrocyte sedimentation rate.
This study was not exent from limitations. First, we gathered data from a single healthcare center, which is contrary to external validity. Second, it uses secondary data and it has not been possible to measure other variables of interest, such as the qualitative analysis of requests. in this regard, it would be desirable to be able to categorize requests as unecessary or inadequate based on context or clinical criteria. Third, we must stress the importance of distinguishing clinical and statistical significance, and correlation and causality when interpreting the results of our analysis; especially because the complexity of the phenomenon under assessment. Patients and their requests, even when living in the same area, are different due to differences in time, base pathologies, seasonality, and the effects of the pandemic. It is also worth mentioning that 2021 patients were affected by the pandemic, while the pre-intervention period is free from such influences, given the first case in Argentina was attested as late as March 4th 2020 [9,16].
However, we must mention that its main strength lies in the production of new, area-specific data for healthcare management, and the fact that rethinking innovative strategies that could reduce the costs of healthcare, which are especially relevant to the current healthcare crisis. We consider simply deactivating the multiple order system was not enough. However, better effects could probably be achieved with a multifacetic intervention, including continual education and formation of healthcare professionals, especially as related to the context of CEA, where there is a high rate of turnover for both professional and resident doctors in training [17], which aim at warning about the potential damage of "low value" interventions and their implications for equality and the use of resources [10]. In this line, new studies are necessary to propose assessment methods based on specific clinical scenarios (for example, acute delirium), which could lead to using clinical practice guidelines for assessing the quality of assesments and detecting the overuse or underuse of complementary studies [18]. Additionally, there are alternative stratagies for healtchare information systems, which are pending in evaluation: (a) putting barriers HCE for requesting high-cost studies, and/or (b) mandatory validation of some specific complementary studies by an area coordinator with signed authorization, and/or (c) restricting access to certain high-cost studies to based on personal and professional conditions, and/or (d) reestructuring batteries for pathologies, consistent with other institutional protocols.
Healthcare costs experienced a sharp decrease in impact during the COVID-19 pandemic. In emergency centrals, facing a grwoing number of patients and having limited resources, computer-based medical order systems have inadvertent economic consequences, which are likely attributable to constant rush, which provides incentives for automatic behaviors and is contrary to critical thinking and clinical criteria.
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Limitations of liability:
Responsibility for this work lies exclusively with those who collaborated in conducting this study.
Conflict of interest:
None.
Funding:
The present work did not receive any funding
Aprobación ética:
La aprobación ética se obtuvo de Comité de Ética institucional (CEPI#5960).
Agradecimientos:
Al Área de Investigación en Medicina Interna y al Departamento de Informática en Salud por el apoyo institucional.
Originality:
This article contains original work exclusively and has not been sent for publication to any other scientific media outlet, nor in partial nor in total form.
Grant of rights:
Those who participated in conducting this study grant authorship rights to Universidad Nacional de Córdoba for publication in Revista de la Facultad de Ciencias Médicas and translation into english.
Authors' contributions:
All authors participated in the design and conduction of this study, collected data and helped in writing the manuscript, and claim responsibility for its content and the contents and approve its final version.
Obra derivada: Traducción del artículo "Efecto de la inactivación de las órdenes múltiples para solicitud de estudios complementarios en una central de emergencias", escrito por Muñoz et al, publicada en Rev Fac Cien Med Univ Nac Cordoba. 2023; 79 (1), realizada por la Revista de la Facultad de Ciencias Médicas de Córdoba
This derivative work is a translation of the article "Efecto de la inactivación de las órdenes múltiples para solicitud de estudios complementarios en una central de emergencias", authored by Muñoz et al, published in Rev Fac Cien Med Univ Nac Cordoba. 2023; 79 (1), produced by Revista de la Facultad de Ciencias Médicas de Córdoba
Recibido: 2022-02-19 Aceptado: 2022-10-06
DOI: http://dx.doi.org/10.31053/1853.0605.v80.n1.36760
https://creativecommons.org/licenses/by-nc/4.0/
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