Implementation of Lean Management in a Multi-Specialist Hospital in Poland and the Analysis of Waste

At the beginning of the 21st century, Lean Management (LM) tools were introduced into the healthcare sector around the world. In Poland, there are still few LM implementations, and they are not of a comprehensive nature. The aim of this article is to present the application of the LM concept in a hospital in Poland as a tool for the identification and analysis of waste and its impact on the process of organizing the provision of medical services on the example of improvements in the process of patient admission. In the period from 1 July 2019 to 31 December 2019, a project of LM implementation was carried out at the Provincial Specialist Hospital in Wroclaw. The project was based on the method of value-stream mapping and 5Why. Standardized interviews (before and after the project) were conducted with people from the hospital management and middle-level managers. The implementation of LM tools resulted in the identification of a number of wastes, which have been divided into groups. The most important waste was paper medical documentation. Its change to an electronic form allowed for a better use of human capital resources; savings included 2.3 nursing positions and 1.09 medical staff positions.

Virtual Dental Care - Pediatric Teledentistry

Teledentistry falls under virtual dental care where use of virtual communication is in between dentist and the patient to provide dental care and education to patients. Patients receive virtual examinations and are provided consultation, diagnosis, treatment planning, guidance, transmission of dental information and education via tele-communications. During this COVID-19 pandemic it has offered a solution to resume dental practice as a tool ensuring safety for all. Pediatric teledentistry helps by saving time, allowing child- dentist interaction before the visit and reducing stress for patients, guardians and dentists. Telescreening and teletriage can be implemented publically into routine dental practice and also as a triage-based emergency management strategy as it provides an effective compromise between limiting patient admission, ensuring their pain control and symptom relief.

Prospective comparison of AMB, GAP AND START scores and triage nurse clinical judgement for predicting admission from an ED: a single-centre prospective study

BackgroundIt is postulated that early determination of the need for admission can improve flow through EDs. There are several scoring systems which have been developed for predicting patient admission at triage, although they have not been directly compared. In addition, it is not known if these scoring systems perform better than clinical judgement. Therefore, the aim of this study was to validate existing tools in predicting hospital admission during triage and then compare them with the clinical judgement of triage nurses.MethodsTo conduct this prospective, single-centre observational study, we enrolled consecutive adult patients who presented between 30 September 2019 and 25 October 2019 at the ED of a large teaching hospital in Milan, Italy. For each patient, triage nurses recorded all of the variables needed to perform Ambulatory (AMB), Glasgow Admission Prediction (GAP) and Sydney Triage to Admission Risk Tool (START) scoring. The probability of admission was estimated by the triage nurses using clinical judgement and expressed as a percentage from 0 to 100 with intervals of 5. Nurse estimates were dichotomised for analysis, with ≥50% likelihood being a prediction of admission. Receiver operating characteristic curves were generated for accuracy of the predictions. Area under the curve (AUC) with 95% CI for each of the scores and for the nursing judgements was also calculated.ResultsA total of 1710 patients (844 men; median age, 54 years (IQR: 34–75)) and 35 nurses (15 men; median age, 37 years (IQR: 33–48)) were included in this study. Among these patients, 310 (18%) were admitted to hospital from the ED. AUC values for AMB, GAP and START scores were 0.77 (95% CI: 0.74 to 0.79), 0.72 (95% CI: 0.69 to 0.75) and 0.61 (95% CI: 0.58 to 0.64), respectively. The AUC for nurse clinical judgement was 0.86 (95% CI: 0.84 to 0.89).ConclusionAMB, GAP and START scores provided moderate accuracy in predicting patient admission. However, all of the scores were significantly worse than the clinical judgement of the triage nurses.

An Intelligent Patient Admission Model of Day Surgery Using Heterogeneous Data with Semi-Supervised Learning: A Case Study of Laparoscopic Cholecystectomy (Preprint)

BACKGROUND Day surgery has many advantages including shortening hospital stay, decreasing the risk of hospital-associated infections, and increasing cost efficiency over traditional surgery, it has gained a great reputation and popularity in recent years. However, the patients’ admission criteria of day surgery at present were mainly based on expert experience, which was a lack of scientific evidence. OBJECTIVE Our study is to investigate the day surgery patient’s admission criteria and build an intelligent machine learning model of day surgery patients who underwent laparoscopic cholecystectomy, to ensure patients’ safety and medical quality, providing reference and inspiration for other day surgery admission decisions. METHODS We analyzed the clinical data of day surgery patients who underwent laparoscopic cholecystectomy at West China Hospital from Jan 1st 2009 to Dec 31st 2021 and developed a semi-supervised artificial intelligence algorithm, SDSPA algorithm, which is built by self-training and uses both structured data like patient characteristics and unstructured clinical diagnosis to assist surgeons to make quick admission decisions. RESULTS After comparing several classifiers with self-training in our experiment, the performance of LightGBM with unstructured text processed by BERT were the best, obtaining an accuracy of 0.85 and an f1-score of 0.83, as well as reaching 0.97 on the precision score, which is an important indicator related to patients’ safety. CONCLUSIONS The application of our SDSPA algorithm can make the patient admission of day surgery more intelligent, and maximize the utilization of medical resources while ensuring patients’ safety.

Information system support for medical secretaries’ work in patient administration tasks in different phases of the care process

Medical secretaries may have several separate electronic nursing information systems in use, but regardless of the systems, their task is to make sure that the patient information is correct and usable. The purpose of this study is to describe the support provided by the hospital information systems for the work of medical secretaries in patient administration tasks in different phases of the care process. The data were collected in a central hospital where medical secretaries had long been using partly electronic information systems. The data were collected using an abridged version of the Hospital Information System Monitor (HIS-monitor). The majority of the secretaries (N=60) gave a positive assessment for the support provided by the information system for their work at patient admission, when ordering diagnostic or therapeutic examinations or procedures, and at patient discharge. In the planning and organization of care, most thought that the systems provided poor support for informing all those involved in patient care. At patient admission, nearly half considered that the support for ensuring data protection (46%) and the systems’ compliance with legal obligations (44%) was poor. In connection with ordering diagnostic and therapeutic examinations and procedures, nearly half (43%) thought that information on the availability in ancillary units was not readily and easily available. At patient discharge, 40% considered that the systems did not support the identification of missing or incorrect information. The hospital information system provides partial support for medical secretaries’ work. The implementation of fully electronic systems and their functions may improve the support.

Effect of covid-19 pandemic on in-hospital mortality

Objective: This study aimed to examine the effect of the pandemic on hospital mortality and patient admission in four months since March 2020 when the Ministry of Health announced the first confirmed COVID-19 case in Turkey and the first wave occurred. Material-Method: This research is a single-centre, retrospective, cross-sectional descriptive study. It covers the periods between March 01 and Jun 30 of 2018, 2019, and 2020. Results: Between 2018-2020, 897522, 972799, and 395438 patients were admitted to our Hospital, respectively. It was observed that the number of admissions decreased by 55-60% in 2020 compared to the previous years (p=0.001). Moreover, 205318 (22.9%) of the admissions in 2018, 229278 (23.6%) of the admissions in 2019, and 1127293 (32%) of the admissions in 2020 were emergency room (ER) admissions. Especially in 2020, there was a significant increase in the overall in-hospital (p=0.001) and ER (p=0.001 mortality rates compared to previous years. In-hospital mortality was found to be higher, especially in patients with suspected COVID-19 (p=0.001). It was found that the number of deaths due to respiratory causes was significantly increased in 2020 compared to the previous years (p=0.001). Conclusion: The COVID-19 pandemic has led to significant changes in mortality rates and causes of mortality compared to previous years. Although the pandemic has affected all healthcare systems, ER and intensive care units (ICU) are seriously affected.