Demand Forecast and Optimal Planning of Intensive Care Unit (Icu) Capacity

Angelo, Simone Aldrey; Arruda, Edilson F.; Goldwasser, Rosane; Lobo, Maria; Salles, André Assis de; Silva, José Roberto Lapa e

doi:10.1590/0101-7438.2017.037.02.0229

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…The outcome of caesarean section during high-risk pregnancy found in this study is similar to the findings obtained in other states in the northeast and southeast Brazil [20, 21]. It is emphasised that caesarean delivery, when performed following medical and scientific indications, contributes to the reduction of maternal and perinatal mortality, especially in high-risk pregnancies [19, 22].…”

Section: Discussionsupporting

confidence: 87%

Access to public health services and integral care for women during the puerperal gravid period period in Ceará, Brazil

Monteiro

Barbosa

Vertamatti

et al. 2019

BMC Health Serv Res

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BackgroundOver time, the Brazilian health system, a growing country, has been developing to ensure good accessibility to health goods and services. This development is focusing on the principle of universality of access and completeness of health care. In this context, we aimed to evaluate the completeness of care and universality of access for women in their pregnancy and puerperal period in Ceará, Brazil.MethodsA descriptive, cross-sectional study based on a quantitative approach, using information collected from the database of the regulation system of the state of Ceará and data from the Prenatal Monitoring System. The research population comprised of 1701 women who delivered a baby in an obstetric reference unit in the Health Macro-Region of Cariri, Ceará, Brazil from January to December 2015.ResultsThere was a high rate of cesarean delivery (49.7%) and a high waiting time for access to high-risk delivery (32.6%) and neonatal intensive care unit (72.9%). There was also a low percentage (41.1%) of pregnant women undergoing an adequate number of prenatal consultations, dental care (20%), educational activities (15%), visits to the maternity ward (0.1%), laboratory tests of the third trimester (29.2%) and puerperal consultation (37.9%).ConclusionsIt was concluded that the Maternal and Child Health Policy, especially the Rede Cegonha, which is still under development, does not ensure access and completeness of care for women during the prenatal, delivery, and puerperal periods, thus violating their reproductive rights. The results of this study allow a critical analysis by the academia and health managers in search of strategies to improve the services of Rede Cegonha in Brazil.

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Section: Discussionsupporting

confidence: 87%

Access to public health services and integral care for women during the puerperal gravid period period in Ceará, Brazil

Monteiro

Barbosa

Vertamatti

et al. 2019

BMC Health Serv Res

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“…SONUÇ COVID-19 salgını neticesinde alınan tedbirler, sağlık personeli, sağlık malzeme ve alt yapı ihtiyaçları ve diğer birçok alanla ilgilidir. Ancak içlerinde en önemlisi hayati tehlike altında olan insanlara hizmet sunumunda yaşanacak eksikliklerdir (3). Bu nedenle, çalışmada yoğun bakım yatak ve ventilatör sayısı açısından eksikliklerimizi tespit etmek ve ihtiyaçların zamanında sağlanmasına yardımcı olmak amacıyla söz konusu gereksinimlerin tahmini üzerinde durulmuştur.…”

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Türkiye’de COVID-19 Salgın Sürecinde İhtiyaç Duyulacak Yoğun Bakım Yatak ve Solunum Cihazı Sayılarının Direkt Tahmini

Ankaralı

2020

Anadolu Kliniği Tıp Bilimleri Dergisi

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Türkiye' de coronavirus vaka sayısının hızlı artış gösterdiği şu günlerde, sunulan sağlık hizmetlerindeki ihtiyaçların ve özellikle hayati önem taşıyan hasta ihtiyaçlarının kanıta dayalı öngörüsü büyük önem taşımaktadır. Bu çalışmada, karşılaştırmalı olarak iki farklı model yardımıyla yoğun bakım yatak ve solunum cihazı sayılarının öngörülmesi amaçlandı. Gereç ve Yöntemler: Veriler, Gompertz ve Zaman serileri modelleri ile incelendi. Bulgular: Çalışmada kullanılan modellerin öngörü başarıları benzer bulundu. Ancak modellemede kullanılan veri sayısının az olması nedeniyle (13 gün) sadece 1 haftalık öngörüler verildi. Gompertz modeline göre 6 Nisan' da öngörülen toplam yoğun bakım yatak sayısı 1474 iken zaman serisi modelinde 1485 bulundu. Aynı tarihte ihtiyaç duyulacak solunum cihazı sayısı Gompertz modeline göre 1000, zaman serisi modeline göre 1004 olarak hesaplandı. Sonuç: Veri sayısı (gün sayısı) 30' un üzerine çıktığı zaman kullanılan matematiksel modeller, eğilimi daha iyi yakalayacağı için ileriye dönük uzun süreli tahminlerdeki başarıları artacaktır.

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“…Congested ICUs also increase waiting time and congestion in the rest of the hospital such as in the emergency department (Mathews et al 2018) or regular inpatient wards (Long and Mathews 2018). Troy and Rosenberg (2009), Angelo et al (2017) incorporates estimates of overall demand for ICU into a simulation framework to inform strategic capacity sizing decisions. In empirical studies, it is not unusual to divide patients depending on whether they need an ICU or not.…”

Section: Referencementioning

confidence: 99%

Predicting Inpatient Flow at a Major Hospital Using Interpretable Analytics

Bertsimas

Pauphilet

Stevens

et al. 2022

M&SOM

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Problem definition: Translate data from electronic health records (EHR) into accurate predictions on patient flows and inform daily decision making at a major hospital. Academic/practical relevance: In a constrained hospital environment, forecasts on patient demand patterns could help match capacity and demand and improve hospital operations. Methodology: We use data from 63,432 admissions at a large academic hospital (50% female, median age 64 years old, median length of stay 3.12 days). We construct an expertise-driven patient representation on top of their EHR data and apply a broad class of machine learning methods to predict several aspects of patient flows. Results: With a unique patient representation, we estimate short-term discharges, identify long-stay patients, predict discharge destination, and anticipate flows in and out of intensive care units with accuracy in the 80%+ range. More importantly, we implement this machine learning pipeline into the EHR system of the hospital and construct prediction-informed dashboards to support daily bed placement decisions. Managerial implications: Our study demonstrates that interpretable machine learning techniques combined with EHR data can be used to provide visibility on patient flows. Our approach provides an alternative to deep learning techniques that is equally accurate, interpretable, frugal in data and computational power, and production ready.

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Demand Forecast and Optimal Planning of Intensive Care Unit (Icu) Capacity

Cited by 14 publications

References 25 publications

Access to public health services and integral care for women during the puerperal gravid period period in Ceará, Brazil

Access to public health services and integral care for women during the puerperal gravid period period in Ceará, Brazil

Türkiye’de COVID-19 Salgın Sürecinde İhtiyaç Duyulacak Yoğun Bakım Yatak ve Solunum Cihazı Sayılarının Direkt Tahmini

Predicting Inpatient Flow at a Major Hospital Using Interpretable Analytics

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