Access the full text.
Sign up today, get DeepDyve free for 14 days.
Article Facilitating Patient‐Centric Thinking in Hospital Facility Management: A Case of Pharmaceutical Inventory 1 2 3 2, 2 2 2 Xiang Xie , Zigeng Fang , Long Chen , Qiuchen Lu *, Tan Tan , Zhen Ye and Michael Pitt Institute for Manufacturing, University of Cambridge, Cambridge CB3 0FS, UK; email@example.com The Bartlett School of Sustainable Construction, University College London, London WC1E 6BT, UK; firstname.lastname@example.org (Z.F.); email@example.com (T.T.); firstname.lastname@example.org (Z.Y.); email@example.com (M.P.) School of Architecture Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK; firstname.lastname@example.org * Correspondence: email@example.com Abstract: Conventional hospital facility management (FM) focuses on reasonably allocating various resources to support core healthcare services from the perspectives of the FM department and hos‐ pital. However, since patients are the main service targets of hospitals, the patients’ demographic and hospitalization information can be integrated to support the patient‐centric facility manage‐ ment, aiming at a higher level of patient satisfaction with respect to the hospital environment and services. Taking the pharmaceutical services in hospital inpatient departments as the case, forecast‐ ing the pharmaceutical demands based on the admitted patients’ information contributes to not only better logistics management and cost containment, but also to securing the medical require‐ ments of individual patients. In patient‐centric facility management, the pharmacy inventory is re‐ garded as the combination of medical resources that are reserved and allocated to each admitted patient. Two forecasting models are trained to predict the inpatients’ total medical requirement at the beginning of the hospitalization and rectify the patients’ length of stay after early treatment. Specifically, once a patient is admitted to the hospital, certain amounts of medical resources are Citation: Xie, X.; Fang, Z.; Chen, L.; Lu, Q.; Tan, T.; Ye, Z.; Pitt, M. reserved, according to the inpatient’s gender, age, diagnosis, and their preliminary expected days Facilitating Patient‐Centric Thinking in the hospital. The allocated inventory is updated after the early treatment by rectifying the inpa‐ in Hospital Facility Management: A tient’s estimated length of stay. The proposed procedure is validated using medical data from eight‐ Case of Pharmaceutical Inventory. een hospitals in a Chinese city. This study facilitates the integration of patient‐related information Buildings 2022, 12, 888. https:// with the conventional FM processes and demonstrates the potential improvement in patients’ sat‐ doi.org/10.3390/buildings12070888 isfaction with better hospital logistics and pharmaceutical services. Academic Editors: Simone Secchi, Nicoletta Setola and Luca Marzi Keywords: patient‐oriented; facility management; logistics management; pharmaceutical services; hospital environments Received: 1 May 2022 Accepted: 21 June 2022 Published: 23 June 2022 Publisher’s Note: MDPI stays neu‐ 1. Introduction tral with regard to jurisdictional The emergence of the COVID‐19 pandemic has exerted tremendous pressure on the claims in published maps and institu‐ operation of hospitals and the delivery of satisfactory healthcare services and hospital tional affiliations. environments to patients since early 2020. Leveraging state‐of‐the‐art digital technologies, the digitalization of the healthcare sector sheds light on a promising pathway towards optimizing the use of medical resources and delivering high‐quality healthcare services and hospital environments [1,2]. The healthcare services that are provided by hospitals Copyright: © 2022 by the authors. Li‐ censee MDPI, Basel, Switzerland. are mainly delivered through two categories of processes: primary processes and infra‐ This article is an open access article structure processes. The primary processes are those directly influencing the patients’ distributed under the terms and con‐ state of health (e.g., performance of operations and intravenous injections), while the in‐ ditions of the Creative Commons At‐ frastructure processes are relevant for the provision of necessary resources (e.g., person‐ tribution (CC BY) license (https://cre‐ nel, drugs and medicines) and deliver the functionality of primary processes through ativecommons.org/licenses/by/4.0/). maintenance and other activities, such as the development, maintenance, security and Buildings 2022, 12, 888. https://doi.org/10.3390/buildings12070888 www.mdpi.com/journal/buildings Buildings 2022, 12, 888 2 of 19 operations of healthcare facilities . Extensive studies have been conducted to assess the quality of healthcare services that are directly delivered through primary processes in hospitals, mostly according to the patients’ perceptions . However, it is still an open question how to raise the effectiveness of healthcare service delivery through better hos‐ pital facility management work processes . Intending to integrate and align the non‐ core services that are required to operate and maintain a business to fully support the core healthcare services, the hospital facility management oversees a series of infrastructure processes, ensuring the 24‐h operation of the hospital to meet patients’ needs, including but not limited to keeping tabs on the inventory and demand of medical equipment and supplies. Although the patient‐centric service atmosphere in hospitals is greatly advo‐ cated, the current research on hospital FM still focuses on efficiency generation and cost savings from the FM departmental and organizational perspective. This leads to better performance with regards to the hospital, but not necessarily for the patients. Patient‐cen‐ tric facility management is becoming an interesting topic for hospitals and relevant stake‐ holders to provide more generalized FM services and better hospital environments, which effectively identify service gaps and satisfy patients. Logistics management in hospitals is a complex process that deals with various ma‐ terial requirements, including pharmacy items (e.g., drugs, medicines, medical devices) and non‐pharmacy items (e.g., office equipment, foods, linens). The procurement, distri‐ bution, and inventory of these resources through more flexible supply chains contribute to better pharmaceutical management and inventory management in hospitals, which are fundamental elements to hospital facility management. Particularly, for supporting phar‐ maceutical services of hospitals, optimal inventory control strategies can proactively min‐ imize the risks of excess stock and stockouts . In this case, most attention has been given to uncertainties and variability in the supply chain, economic constraints, the availability of human resources to place orders, receive deliveries, and store goods, which stands from the point of view of the FM department in hospitals . On the other hand, this paper discusses the necessity of bringing patient information into the logistics/inventory man‐ agement and facilitating better facility management and corresponding supply chains from patients’ perspective, treating the pharmacy inventory as the aggregated medical resources reserved for individual patients. The patient‐centric facility management per‐ spective helps to further reduce the inventory level and total inventory cost of pharmacy items by modelling the relationships between the demand of pharmacy items in a hospi‐ tal, and the diagnosis, estimated length of stay and other patient information. The rest of this paper is organized as follows. Section 2 reviews patient‐centric facility management, logistics management, and a pharmaceutical service based on patient‐cen‐ tric thinking. Section 3 presents a patient‐centric pharmaceutical service and the two ac‐ companying forecasting models. Section 4 uses a case study to demonstrate the patient‐ oriented pharmaceutical management leveraging the demographic and hospitalization information of admitted patients, while Section 5 concludes the paper. 2. Literature Review 2.1. Patient‐Centric Facility Management in Healthcare Environment The exploration of how FM can be improved and differentiated in a healthcare envi‐ ronment is not new in the academic field [8–13]. However, it was not until recently that researchers have paid attention to investigating how patients and their demographic data can be utilized to provide a patient‐centric FM . Patient‐centric facility management in this paper is defined as “the integration of processes within a healthcare organization to deliver and maintain required services which support efficient and timely medical care activities to meet patients’ needs.” This is a synthetic definition that combines the Nor‐ malization definition of FM  (“the integration of processes within an organization to maintain and develop the agreed services which support and improve the effectiveness of its primary activities”) and Yavorskyi’s  patients‐centric criteria for the healthcare Buildings 2022, 12, 888 3 of 19 sector (“include effective and timely medical care, efficient use of the resources, meeting the patients’ needs and treatment efficiency”). In other words, patient‐centric FM aims to meet the patients’ needs by providing healthcare FM service support without interrupting the medical emergency, surgical, specialized chronic care, and rehabilitation services. It is worth noticing that healthcare FM services frequently share quite different characteristics from the other commonly encountered FM environments (e.g., residential buildings, gen‐ eral‐purpose offices, etc.) in the following two ways. Firstly, the main content of the hos‐ pital FM service is different from other general building types of FM services. For instance, the operation cost of “sterile goods supply” and “maintenance of medical equipment” could take up 42 per cent and 14 per cent of a typical hospital project, which will not be found in an office building’s FM service expenditure . Secondly, the service standard of healthcare FM is higher than the other general types of building FM services. Taking sterile goods supply as an example, once a surgical kit is opened, no matter its size, the untouched content needs to be sterilized and repacked in any case. The cost of supplying sterile goods is dominated by content (i.e., the surgical operation type) and the quantity, rather than the length of the procedure . Several important characteristics of FM in the hospital environment are highlighted above. These characteristics make patient‐centric FM vital for the digital‐era hospital en‐ vironment. Although the majority of hospital FM consumables are manufactured physical goods that are produced and consumed at different times, some healthcare FM services (e.g., catering services) are delivered and consumed simultaneously . Therefore, it is essential to collect and analyse the patients’ entry and hospitalization information to pre‐ dict the busy and off seasons more accurately, in order to ensure that the stocks are suffi‐ cient for both medical and non‐medical consumables. Next, it is also crucial for integrating the patient’s demographic or personal properties into the service delivery process. For example, the nutrition, medical consumables, ventilation and indoor air quality require‐ ments for patients with different types and stages of diseases vary significantly. Accurate forecasting of the patients’ demand for different medical and non‐medical consumables is vital in predicting the aggregate FM service demand within a specified time. Lastly, patients are always the main service objects for healthcare facility management in hospi‐ tals. Again, taking the hospital’s catering service as an example, some patients are likely to require a special meal due to the specific diet style or allergy, making the service more customized to patients’ needs rather than just providing a standardized FM product. It is suggested that patients’ diverse perspectives should be valued in designing FM processes . With the patient‐centric FM thinking involved, bottleneck problems can be better tackled in the facilities management process of hospitals. Firstly, during healthcare‐ser‐ vice delivery, long waiting times are often treated as a symbol of low service quality . Patients are still suffering long waiting times, “bed‐blockers”, and under‐maintained fa‐ cilities (e.g., the unplanned maintenance of operating theatres), which negatively impact their quality of life due to their potentially worsening condition when waiting to receive treatment. Unplanned maintenance and equipment malfunction are two of the main causes of such issues. It is suggested that the facility manager lacks the patients’ demo‐ graphic information, such as length of stay, which stops the FM manager from further predicting the hospital facilities’ utilization level to make a better facility maintenance plan that minimizes the disruption of the hospital’s specialized medical activities. The second aspect of improvement is communication. It is suggested that the communication with the hospital supply chain partners could also be further improved with more fre‐ quent contact [19,20]. Digital logistics management should be designed to integrate pa‐ tients’ information in a timely manner, allowing for more agile hospital logistics manage‐ ment. The lack of information integration is a major obstacle for patient‐centric FM in a digital logistics management environment. In the current hospital information manage‐ ment framework, there is only very limited patient‐centric information communication between the major medical department and the FM department. However, without the Buildings 2022, 12, 888 4 of 19 information integration between the different departments, the department‐wise decision is frequently more focused on the short‐term benefits of their own department, rather than the long‐term benefits of the entire organization/hospital . In summary, the new dig‐ itized hospital logistics management environment should integrate FM information flow with patient‐related information flow inside the hospital (e.g., pharmaceutical infor‐ mation flow). 2.2. Logistics Management and Pharmaceutical Service With the increase in public health emergencies and the ageing population, the surge in healthcare demand poses severe challenges to the capabilities of healthcare infrastruc‐ ture [22,23]. The management capability to suddenly increase inpatients has a significant impact on the healthcare facility resilience, such as the bed‐occupancy levels and the sta‐ bility of flows into the hospitals. One of the main factors for FM is the patients’ admission time. For example, prioritizing and differentiating the admission time of patients with different symptom levels can greatly relieve the capacity and management pressure of healthcare facilities and FM during the COVID‐19 period. Furthermore, to provide satis‐ factory health services and hospital environments, optimizing healthcare resource supply and allocation is one of the most significant global issues in terms of sustainability and equity . Healthcare logistics stands for an integrated approach to healthcare supply‐ chain design, holding the potential to deal with the emerging demand shortage and effi‐ ciency improvement needs from the hospital FM perspectives . In hospitals, logistics management consists of conventional physical flows, associated information flows and other emerging flows, such as patients, throughout the care chain . Supply chain man‐ agement is the backbone of these healthcare deliveries. The practice of the healthcare sup‐ ply chain is defined as “a set of activities undertaken in an organization that helps the organization in the effective management of its supply chain by integrating its stakehold‐ ers such as manufacturers, distributors, suppliers and customers” . By supporting the supply chain with more focus on patients, especially the admission time of patients, the healthcare logistics management can flexibly adjust the stock and promote the efficiency of the hospital pharmacy and FM. Although various healthcare tasks (e.g., diagnosis, treatment, and management) put pressure on logistics, they also generate a large scale of daily datasets [12,28]. Capturing added value from these datasets is expected to benefit the hospital information flows and further improve the healthcare supply chain and op‐ erational efficiency . Besides, the adoption of digital technologies and methods can transform how patients receive healthcare services by utilizing these datasets for logistics improvement schemes. In practice, healthcare organizations are trying to acquire these advanced capabilities to improve logistics management in terms of finance, administra‐ tion and medicine. Existing studies have widely explored potential solutions to advance these capabili‐ ties. However, few of them focus on patient‐centric solutions. Further, various challenges arise from (1) logistic issues; (2) digitalization issues. For healthcare logistics, the main challenges that are identified in the literature include overstocking , inventory shrink‐ age , fragmented processes , delivery delay  and unpredictability . These challenges reflect that many processes are still paper‐based labour‐intensive activities in healthcare logistics and lack standardization, visibility, and traceability. Conventional ap‐ proaches to healthcare logistics prevent the capture of advanced capabilities. The main hurdles to digital transformation in healthcare services involve technological factors, such as information flow integration/sharing, data capturing that is associated with patients and personal health, algorithms, performance‐measurement tools, and nontechnological barriers, such as cultural resistance, organizational structure and governance . These phenomena reflect that the introduction of digital technology is still a rhetoric‐based ini‐ tiative. Further, there is still long way to go for the industry to fully embrace digitalization, which is not a last mile problem. With the progress of digitalization, if these obstacles are Buildings 2022, 12, 888 5 of 19 not resolved appropriately, they may fail the transformation, and the operational effi‐ ciency may be even worse than conventional logistic methods. The aim of pharmaceutical logistics is “placing the right drugs and medical supplies, in the right quantities, in the right conditions, at the right health service delivery points, at the right time, for the right patients/users and for the right cost” . There is a four‐ step process from the acquisition in the market to the distribution to wards: (1) drug re‐ ception and warehouse/pharmacy operations; (2) request and validation; (3) transporta‐ tion; (4) ward drug management . However, there are existing conventional logistics management problems. For example, improper hospital drug inventories may cause shortages or stagnant supplies. Besides, traditional logistics basically occurs without the assistance of digital technologies. Subsequently, many studies notice that introducing in‐ formation and communication technology will be an essential strategy to improve patient safety and reduce drug consumption. Some techniques have been introduced into logis‐ tics management to facilitate the efficiency improvement of pharmaceutical services, in‐ cluding geographic information systems , beacon technology , unmanned aerial vehicles , blockchain  and so on. A series of advanced digital approaches are nec‐ essary to develop enhanced analytical capabilities for the existing conventional logistics management problems that are mentioned above, one of which is to accurately forecast the pharmaceutical demands for optimizing the supplies and inventory of drugs, medi‐ cines, and medical devices. Some studies have applied advanced computing techniques to forecast, plan or mon‐ itor the pharmaceutical supply chain. Qin et al.  introduced cluster computing to mon‐ itor and manage drug logistics. Ershadi and Ershadi  proposed a multi‐objective opti‐ mization model to plan the pharmaceutical supply chain. Du et al.  combined the ge‐ netic algorithm with a BP neural network for drug inventory management. Nikzad et al.  proposed a two‐stage stochastic programming approach for drug inventory routing problems. Saedi et al.  developed a stochastic optimization framework to find the opti‐ mal drug stock levels and order quantity levels. However, existing studies ignore the fact that hospital service aims to serve patients [44,45,46,47]. The patient‐centric FM perspec‐ tive is vital in this sense, and it is expected to improve hospital pharmacy inventory‐man‐ agement efficiency, especially for the inpatient departments in hospitals. Thus, there is a research gap to develop a patient‐centric demand‐forecast procedure for estimating the total amount of pharmaceutical services and products that are needed by integrating dig‐ ital methods, logistics management and patient‐centric FM thinking. 3. Patient‐Centric Pharmaceutical Services To respond to the clinical requests coming from different departments (e.g., inpa‐ tients, outpatients, operating rooms, etc.), the stocks of drugs and medicines need to be controlled cautiously to satisfy various demands from patients. Moreover, considering the high purchase and stocking costs of many drugs and medicines (e.g., the need to be kept in refrigerated storage rooms) and the shortage of staff working in pharmacies, the phar‐ macy inventory must match the corresponding demands to avoid the expiration and un‐ necessary immobilization of medical resources . In this study, to optimize the drug and medicine supplies and consequently improve the pharmaceutical services that are delivered to patients for the inpatient departments in hospitals, a demand forecasting pro‐ cedure integrated with patient‐centric FM thinking is proposed to reduce the risk of excess stock and drug shortage, which endanger hospital profitability and the patient experience. Instead of focusing on the historical demand data and using time series forecasting models (e.g., moving average, exponential smoothing) to recognize data patterns, this study uniquely proposes a patient‐centric demand forecast procedure to guide the adjust‐ ment of supplies and inventories, and further guarantee the pharmaceutical services. In this procedure, the inventory in the hospital inpatient department is regarded as the com‐ bination of the drugs or medicines that are allocated to every single patient. The drugs and medicines are explicitly claimed by specific patients until being released after they Buildings 2022, 12, 888 6 of 19 are discharged from the hospital. It is assumed that the supporting supply chain ensures the pharmacy inventory always satisfies the need of all these patients without surplus stock. To achieve this goal, this study developed two forecasting models based on the Support Vector Regression (SVR) under two study scenarios, as shown in Table 1. The first forecasting model uses input variables of inpatient demographic and initial hospital‐ ization information, including expected days in the hospital, gender, age, and diagnosis type, to predict the inpatients’ total medicine consumption during the hospitalization. The second forecasting model predicts the amended and updated patient’s length of stay us‐ ing all demographics, hospitalization, and the patient’s early‐stage medical consumable information (i.e., resuscitation medicine and surgical related consumables). The SVR model is adopted due to its suitability for small sample‐size learning. The mathematical form of the nonlinear SVR is given as follows, in which the K is the Gaussian Radial Basis function. 𝑓 𝑢 𝛽 𝛼 𝐾 𝑥 ,𝑢 | | 𝐾 𝑥 ,𝑢 𝑒 Table 1. Two forecasting models for estimating patient medical consumption. Study Scenario Input Variables Output Variables Patient (estimated) days in hospital Medicine consumption (counts) Patient gender Medicine consumption (quantity) Early consumption al‐ location Patient age Medicine consumption (cost) Patient disease type ‐ Patient resuscitation medicine and surgical re‐ Updated patient length of stay lated consumables (counts) Patient resuscitation medicine and surgical re‐ ‐ lated consumables (quantity) Updated length of stay Patient resuscitation medicine and surgical re‐ ‐ and allocation lated consumables (cost) Patient gender ‐ Patient age ‐ Patient disease type ‐ Figure 1 explains the detailed implementation of the demand forecasting procedure that is integrated with patients’ information using these two forecasting models. Once a patient is admitted to the hospital inpatient department, the first forecasting model is adopted to preserve the drugs and other medical resources, according to the gender, age, disease type, and the initial estimated days of hospitalization. Preserved drugs and med‐ ical resources (early allocation) are claimed and allocated to the use of the specific patient only. After the early treatment, the second forecasting model is utilized to rectify the pa‐ tient’s estimated length of stay and update the allocated drugs and medical resources (up‐ dated allocation) based on the resuscitation medicine or surgical related consumables that are used during the early treatment. The remaining drugs and medicines are released to the inventory as unused resources once the patient is discharged from the hospital. Using this procedure, the pharmacy inventory becomes patient‐centric, and the drugs and med‐ icines that are used by specific patients are claimed and rectified based on the inpatient’s demographic and pathogenetic condition. With the help of the supporting supply chain, the hospital pharmacy can run more efficiently by flexibly adjusting the stock, according to the demands of all admitted patients. Buildings 2022, 12, 888 7 of 19 Figure 1. Patient‐centric drug and medicine demand forecasting procedure. Alongside the forecasting models for these two study scenarios, a correlation analysis is conducted to reveal the dependencies between the use of different drugs and medical resources. The goal of this correlation analysis is to identify the groups of medical con‐ sumable “packages” that are used by patients in bundles. This can help the hospital’s pharmacy department to control the drugs and medical supplies with better logistics man‐ agement. Moreover, the proposed procedure is particularly useful in the face of the COVID‐19 pandemic. On the one hand, this is an emerging infectious disease that lacks sufficient historical data for directly estimating the hospital‐level demand. By estimating the indi‐ vidual demands and aggregating the individuals into the overall hospital demand, it is more feasible to make best use of the patient data, since significant divergences exist be‐ tween patients due to demographic characteristics . On the other hand, during COVID‐ 19, the days of inventory and stock turns become much shorter due to the high demand. Estimating the patient‐level demand potentially leads to a faster response to patients’ needs . 4. Case Study 4.1. Research Data The two forecasting models that were trained within this study utilized the data from inpatients who were admitted to eighteen different hospitals in a southeast coast city of China. The data range from 14 April 2019 to 30 April 2021, with the diagnosis of myocar‐ dial infarction (MI) and tuberculosis (TB), referred to as I21 and A15 in the International Classification of Diseases, Tenth Revision (ICD‐10) Codes. The reimbursement records of 1571 inpatients have been extracted from hospitals’ electronic health records system (in‐ cluding the demographic information, hospitalization information, and consumable count, quantity, and consumption information). Except for variables with the consumable count, quantity, and consumption information, all the other variables are independent and contain the patient’s activities during the hospitalization. The demographic infor‐ mation, hospitalization information variables, and a sample of the consumable count (the number of charges), quantity (the number of consumables), and consumption (indicates the monetary cost of the consumables) information are summarized in Table 2. Table 2. Study’s raw data. Category Number of Variables Specific Variables Demographic information 3 Inpatient number, age, gender Personnel number, days in hospital, hospital code, name of primary diagnosis, master diagnostic code, Hospitalisation information 13 project/consumable number, project/consumable name, project/consumable specification, unit of Buildings 2022, 12, 888 8 of 19 project/consumable, project/consumable unit price, project/consumable quantity, total bill cost, bill number Aspirin, clopidogrel hydrogen sulfate, Tegretol, atorvastatin, rosuvastatin, metoprolol succinate, Consumable count, quantity, and con‐ captopril, enalapril, benazepril, nitroglycerin, coro‐ 4018 sumption information nary balloon dilation catheter, coronary stents, di‐ agnostic guidewire, intravascular angiography catheters, etc. 4.2. Data Pre‐Processing The dataset that is used for this experiment only contains the myocardial infarction inpatient information. Table 3 shows the characteristics of the mentioned variables with their corresponding proportion or mean (standard deviation). These variables are classi‐ fied into demographic, hospitalisation, and consumable quality & cost information. The major characteristics of this study’s cohort were male (78.49%) with the patients’ mean age at 63.13. From the entire dataset, 90% was used as the training set for tuning and train‐ ing the model, while the remaining 10% was used as the test dataset. Table 3. Variables of the dataset. Factors Proportion or Mean Demographic information Sex, male (%) 78.49 Age [mean (SD)] 63.13 (15.92) Hospitalisation information Days in hospital Less than 10 days (including upper boundary) (%) 68.36 Between 10 and 20 days (including upper boundary) (%) 25.97 Between 20 and 30 days (including upper boundary) (%) 3.69 Above 30 days (excluding lower boundary) (%) 1.97 Average days in hospital (before 2020/04/15) [mean (SD)] 9.35 (7.51) Average days in hospital (after 2020/04/15) [mean (SD)] 9.97 (7.78) Total number of project/consumables per patient (under unique inpatient number) 1580.91 (3200.27) Average number of project/consumables per patient per day (under unique inpatient number) 158.1 (180.39) Total cost of project/consumable per patient (under unique inpatient number) 51,449.69 (45,193.50) Average cost of project/consumable per patient per day (under unique inpatient number) 6620.46 (7313.68) Consumable quantity & cost information (selected) Average count of aspirin per patient [mean (SD)] 1.237 (2.163) Average quantity of aspirin per patient [mean (SD)] 19.249 (13.858) Average count of atorvastatin per patient [mean (SD)] 0.950 (1.870) Average quantity of atorvastatin per patient [mean (SD)] 11.167 (13.348) Average count of metoprolol succinate per patient [mean (SD)] 0.752 (1.900) Average quantity of metoprolol succinate per patient [mean (SD)] 7.947 (11.432) Average count of captopril per patient [mean (SD)] 0.012 (0.115) Average quantity of captopril per patient [mean (SD)] 0.037 (0.489) Average count of nitroglycerin per patient [mean (SD)] 0.934 (0.625) Average quantity of nitroglycerin per patient [mean (SD)] 5.233 (14.183) Average count of coronary balloon dilation catheter per patient [mean (SD)] 0.326 (0.764) Average quantity of coronary balloon dilation catheter per patient [mean (SD)] 0.344 (0.834) Average count of diagnostic guidewire per patient [mean (SD)] 0.404 (0.566) Average quantity of diagnostic guidewire per patient [mean (SD)] 0.491 (0.733) Buildings 2022, 12, 888 9 of 19 Before the primary screening, variables that had insufficient information entries (e.g., the consumables that were consumed by less than 20 different inpatients) were unpicked. In the end, there are 21 variables left that will be used as the input for the forecasting model. After the primary screening of the acquired data, it is suggested that variable in‐ consistency issues and unclassified activity issues need to be resolved for the collected raw dataset. Therefore, three pre‐processing activities have been carried out to make the raw data ready for the model training. First, the consumable specifications and names for the same type of medicine are not consistent. For instance, both “Nitroglycerin” and “Ni‐ troglycerin injection” have been used for the name of the same item. Therefore, the first step of the data preparation process is to standardize the name of medicine or other med‐ ical consumables with common main ingredients to use the same name. Second, it is found that the medical activities, medicines, and other medical consumables share the same var‐ iable category, which hinders the dataset’s capability to conduct more advanced data anal‐ ysis. As a result, works have been carried out to separate all the data entries into three groups: medical activities, medicines, and other medical consumables. Finally, works have also been carried out to eliminate the rarely occurred activities/consumables/medi‐ cines intake. 4.3. Pharmaceutical Demand Forecasting Results Using patient‐oriented thinking, three main findings can be summarized from the regression result of the early consumption allocation study (the first scenario in Table 3). The regression models’ results for medicines’ counts and quantity output are opti‐ mistic, with a satisfied root mean squared error (RMSE) and mean absolute error (MAE); The regression results vary significantly across the different drug categories. The multi‐output regression result for angiotensin‐converting enzyme inhibitors (includ‐ ing captopril, enalapril, benazepril, and ramipril) used for myocardial infarction have the lowest RMSE and MAE (Table 4). This is partially due to the infrequent use of the angiotensin‐converting medicines, which is supported by their relative low R squared scores; The performance of the different medicines is more consistent for the “Counts” and “Quantity” output rather than the “Cost” output. For instance, aspirin has a higher mean square error (at 201.575) than that of clopidogrel hydrogen sulfate (at 110.349) when using the quantity output. However, the mean squared error of aspirin (at 47.561) is much lower than that of clopidogrel hydrogen sulfate (at 4944.783) when using the cost as the output variable unit. There are several reasons behind this: (1) the difference in the unit price of the different manufacturers; (2) the difference in the composition produced by the different manufacturers; (3) the potential price differ‐ ence due to the drug dosage forms. The result suggests that: (1) the general result for the medicine demand forecast is satisfied; (2) the regression forecast errors for angi‐ otensin‐converting enzyme inhibitors are significantly lower than the rest of the med‐ icine groups; (3) the forecast for a better medicine quantity intake forecast might not necessarily lead to a better medicine cost forecast. Buildings 2022, 12, 888 10 of 19 Table 4. Early consumption allocation scenario: multi‐output regression forecast results for 10 typical medicines. Measure Aspirin Clopidogrel Hydrogen Sulfate Tegretol Atorvastatin Calcium Rosuvastatin Metoprolol Succinate Captopril Enalapril Benazepril Ramipril Root mean squared er‐ 2.960 1.517 2.555 2.781 1.251 2.584 0.218 0.200 0.525 0.250 ror (counts) Mean absolute error 0.851 0.528 0.830 0.941 0.457 0.940 0.208 0.200 0.258 0.223 (counts) Root mean squared er‐ 14.198 10.505 21.973 12.136 9.038 11.368 0.253 0.200 2.839 2.749 ror (quantity) Mean absolute error 10.318 4.175 16.977 9.069 3.989 6.710 0.214 0.200 0.636 0.685 (quantity) Root mean squared er‐ 6.896 70.319 194.506 63.510 41.369 24.343 0.180 0.200 7.802 9.781 ror (cost) Mean absolute error 4.944 25.121 145.998 39.581 14.180 14.366 0.180 0.200 1.522 1.798 (cost) Buildings 2022, 12, 888 11 of 19 Regarding the regression result for the updated length of stay and allocation study (the second scenario in Table 3), the updated patient length of stay does not vary signifi‐ cantly when using different input medical and surgical‐related consumable variables (count, quantity or cost). As shown in Table 5, it gives similar RMSE and MAE for the patient’s expected and updated days in the hospital, which indicates that the resuscitation medication and surgical‐related consumable variables can accurately reflect the severity level of patients and give informative clues to their estimated length of stay. Table 5. Updated length of stay scenario: regression forecast results for length of stay. Measures Days in Hospital Root mean squared error (counts) 6.628 Mean absolute error (counts) 4.220 Root mean squared error (quantity) 6.561 Mean absolute error (quantity) 4.107 Root mean squared error (cost) 6.688 Mean absolute error (cost) 4.102 Overall, through the comparison of the different test input and output data variable (Quantity) profiles, some medical consumables, such as nitroglycerin, seem to provide more slightly better support for the forecast of the patients’ expected days that will be spent in hospital. 4.4. Correlation Analysis Results Various medicines are used during the treatment of myocardial infarction. In many cases, some medicines are used complementarily or as the substitute to other medicines with similar effects. The correlation analysis is conducted to calculate the correlation co‐ efficient values between different medicines inside a single medication group or across the group portfolio and reveal the complement and substitute relationship of the different medicines. The selected groups of consumables are: (1) drugs for the prevention of throm‐ bosis (in coral blue); (2) drugs for the lowering of cholesterol (in deep peach); (3) receptor clocker drugs (first scenario in Tables 6 and 7); (4) resuscitation drugs (in silver chalice); (5) coronary intervention equipment (in sea mist); angiography equipment (in salad green) (second scenario in Tables 8 and 9). According to Tables 6 and 7, the average cor‐ relation coefficient for using the counts of appearance output is obviously higher than the average correlation coefficient using quantities as the output variable, which might be re‐ lated to the prescribing habit of specialist doctors, the standard treatment process, and patients’ different conditions. Although the correlation values are quite different when using counts and quantities for analysis, consistent conclusions can be made based on the correlation analysis results. For instance, the correlation coefficient of clopidogrel hydro‐ gen sulfate and Tegretol is negative (at −0.084 (counts) and −0.356 (quantity)), which indi‐ cates that these two medicines are substitute for each other. While still inside the same anti‐thrombotic drugs group, the correlation coefficient values of the aspirin against both clopidogrel hydrogen sulfate and Tegretol are positive, revealing the relationship between these two connections to be complementary. It is also suggested that a higher correlation coefficient value in the concurrent appearance count will not necessarily lead to a higher correlation coefficient value in the quantity of medicine used. For instance, the correlation coefficient of atorvastatin calcium and metoprolol succinate score the highest at 0.781 us‐ ing the count of appearance, while their correlation coefficient only reaches 0.21 when the quantity variables are calculated. For facilitating better pharmaceutical services, correla‐ tion relationships on quantities rather than counts can be used to guide the pharmaceuti‐ cal inventory, considering the complement and substitute relationship between medi‐ cines. Therefore, it is suggested that using the quantity variable might be able to identify and determine some hidden but highly valuable relationships between the different Buildings 2022, 12, 888 12 of 19 medicines for configuring the pharmaceutical inventories. For instance, in Table 9, the correlation coefficients between nitroglycerin and other consumables are very small or even negative in terms of the quantity, while in Table 8, the correlation coefficients are much larger in terms of count. The found relationships can guide the pharmaceutical in‐ ventory, which is further discussed in the next section. Buildings 2022, 12, 888 13 of 19 Table 6. The comparison of the correlation analysis results for medicines used for Scenario One’s study using counts variable. Clopidogrel hydro‐ Aspirin Tegretol Atorvastatin calcium Rosuvastatin Metoprolol succinate gen sulfate Aspirin 1.000 Clopidogrel hydrogen sulfate 0.696 1.000 Tegretol 0.547 −0.084 1.000 Atorvastatin calcium 0.652 0.534 0.417 1.000 Rosuvastatin 0.387 0.305 0.231 −0.113 1.000 Metoprolol succinate 0.765 0.611 0.510 0.781 0.230 1.000 Mean (per inpatient number by col‐ 1.237 0.561 0.820 0.950 0.327 0.752 umn) Standard deviation (per inpatient num‐ 2.163 2.019 1.417 1.870 1.135 1.900 ber by column) Average correlation 0.431 The ratio of average quantity of consumable between four selected consumables (scaled based on the smallest average consumable) 3.79:1.72:2.51:2.91:1:2.30 Table 7. The comparison of the correlation analysis results for medicines used for Scenario One’s study using quantity variable. Clopidogrel hydro‐ Aspirin Tegretol Atorvastatin calcium Rosuvastatin Metoprolol succinate gen sulfate Aspirin 1.000 Clopidogrel hydrogen sulfate 0.198 1.000 Tegretol 0.557 −0.356 1.000 Atorvastatin calcium 0.279 0.220 0.139 1.000 Rosuvastatin 0.275 0.014 0.350 −0.355 1.000 Metoprolol succinate 0.377 0.232 0.256 0.210 0.124 1.000 Mean (per inpatient number by col‐ 19.249 6.188 20.012 11.167 5.162 7.947 umn) Standard deviation (per inpatient num‐ 13.858 13.721 21.742 13.348 10.932 11.432 ber by column) Buildings 2022, 12, 888 14 of 19 Average correlation 0.168 The ratio of average quantity of consumable between four selected consumables (scaled based on the smallest average consumable) 3.73:1.20:3.88:2.16:1:1.54 Table 8. The comparison of the correlation analysis results for resuscitation medicine and surgical related consumables used for Scenario Two’s study using counts variable. Coronary balloon PTCA dilatation Diagnostic guide‐ Intravascular angi‐ Nitroglycerin Coronary stents Contrast syringe dilation catheter catheters wire ography catheters Nitroglycerin 1.000 Coronary balloon dilation catheter 0.145 1.000 Coronary stents 0.326 0.397 1.000 PTCA dilatation catheters 0.289 0.003 0.576 1.000 Diagnostic guidewire 0.295 0.528 0.327 0.174 1.000 Intravascular angiography catheters 0.385 0.309 0.373 0.336 0.528 1.000 Contrast syringe 0.252 0.495 0.313 0.135 0.640 0.485 1.000 Mean (per inpatient number by col‐ 0.934 0.326 0.822 0.773 0.404 0.500 0.291 umn) Standard deviation (per inpatient 0.625 0.764 0.935 1.025 0.566 0.506 0.460 number by column) Average correlation 0.348 The ratio of average quantity of consumable between four selected consumables (scaled based on the smallest average consumable) 3.21:1.12:2.83:2.66:1.39:1.72:1 Buildings 2022, 12, 888 15 of 19 Table 9. The comparison of the correlation analysis results for resuscitation medicine and surgical related consumables used for Scenario Two’s study using quantity variable. Coronary balloon PTCA dilatation Diagnostic guide‐ Intravascular angi‐ Nitroglycerin Coronary stents Contrast syringe dilation catheter catheters wire ography catheters Nitroglycerin 1.000 Coronary balloon dilation catheter −0.055 1.000 Coronary stents 0.023 0.332 1.000 PTCA dilatation catheters 0.046 0.032 0.621 1.000 Diagnostic guidewire −0.024 0.508 0.281 0.180 1.000 Intravascular angiography catheters −0.029 0.289 0.334 0.301 0.479 1.000 Contrast syringe −0.004 0.469 0.258 0.134 0.642 0.453 1.000 Mean (per inpatient number by col‐ 5.233 0.344 0.977 0.919 0.491 0.521 0.351 umn) Standard deviation (per inpatient 14.183 0.834 1.147 1.288 0.733 0.546 0.600 number by column) Average correlation 0.251 The ratio of average quantity of consumable between four selected consumables (scaled based on the smallest average consumable) 14.92:0.98:2.79:2.62:1.40:1.48:1 Buildings 2022, 12, 888 16 of 19 4.5. Improving the Hospital FM and Hospital Environments with Patient‐Centric Data Analysis In this section, the focus is on discussing how the pharmaceutical forecasting results and correlation analysis results can be used to benefit the FM (e.g., pharmaceutical logistic management) and its efficiency in providing better services and hospital environments to patients. 4.5.1. Benefits of the Healthcare Logistic Management with Pharmaceutical Forecast and Correlation Analysis Given the results of the pharmaceutical demand forecast and correlation analysis in Sections 4.3 and 4.4, the forecast and correlation coefficient of the target groups of con‐ sumables and the ratios of averaged consumable quantity can be obtained after analysing the inpatient demographic and hospitalization data. It is implied that the typical ratio of consumables’ average quantity can be used as the checking criteria for the “consumables bundles”. According to the absolute correlation coefficient values, some pairs of consum‐ ables with a coefficient larger than 0.5 (indicating significantly correlated), such as diag‐ nostic guidewire and contrast syringe, can be treated as the “consumable bundle”. Taking diagnostic guidewire and contrast syringe as an example, the correlation coefficient of these two consumables is 0.642 (highly correlated), which means they can be treated as a package. Therefore, their ratio of average quantity (1.40:1) can be used as the benchmark to set the automated stock alarm system for the storage of consumables. When the diag‐ nostic guidewire demand for a specific period is high (given by the early‐consumption allocation prediction), the low stock rate of diagnostic guidewire will not only trigger the procurement reminder for diagnostic guidewire, but also check and remind (if necessary) the stock and procurement process of contrast syringe, according to their ratio of average quantity. It is also implied that this consumable stock’s auto‐checking mechanism can remind the restock of potentially underlying correlated items belonging to a different medical consumable categorization group. For instance, the diagnostic guidewire and coronary balloon dilation catheter belong to angiography and coronary intervention‐related con‐ sumables, which is different from diagnostic guidewire and contrast syringe that both belong to the same category (angiography). Using inpatients’ demographic and hospital‐ ization data, these hidden or implicit correlations can be revealed and considered by fa‐ cility managers. 4.5.2. Benefits of the Estimated Patient Length of Stay In addition to promoting better pharmaceutical services and hospital environments to inpatients, the demographic and hospitalization information of these patients can also help the facility managers better plan facilities’ preventive maintenance and workspace management. Given the prediction results of patients’ length of stay, facility managers will be able to anticipate the busyness level of the hospital in general and the busyness level of corresponding facilities serving specific inpatient groups with specified disease types. This is because the use of many hospital facilities is mostly disease oriented. The number of patients for a specific group of diseases will determine the utilization level of these facilities. As a result, given the analysis result of patients’ demographic and hospi‐ talization data, facility managers are provided with more supportive information to make the operation and maintenance decision with a better order of priority. For example, more accurate forecasting of the inpatients’ length of stay helps to determine the capacity of the hospital to admit new patients and contributes to improving the turnover rate of hospital beds and other facilities. Buildings 2022, 12, 888 17 of 19 5. Conclusions With the rapidly increasing healthcare demand and seriously constrained medical resources, more attention has been focused on optimizing the usage of medical resources and the delivery of high‐quality healthcare services and hospital environments. The non‐ core facility management in the healthcare environment, which is critical to supporting and improving the effectiveness of the primary medical care activities, are highlighted in this paper. The definition of patient‐centric facility management is explored, and the chal‐ lenges and opportunities of using patient‐centric FM thinking in hospital logistics man‐ agement are discussed. Specifically, to facilitate better pharmaceutical services, the phar‐ macy inventory is considered as the aggregated medical resources (e.g., medicines) that are reserved and allocated to each patient. With this patient‐centric view, the inventory of medicines is targeted at satisfying the needs of all patients without too much surplus stock. Two forecasting models are established to integrate patients’ demographic and hos‐ pitalization information into the inventory control strategies. The first model predicts the patients’ total medicine consumption based on their demographic and initial hospitaliza‐ tion information, while the second model updates the patient length of stay, leveraging their usage of resuscitation medicine and surgical‐related consumables. Based on the 2‐ year data from inpatients that were admitted to eighteen hospitals in a south‐eastern China city, it was demonstrated that the incorporation of medical information from pa‐ tients does contribute to an accurate forecast of the pharmaceutical demands and leads to a more balanced inventory, considering the complement and substitute relationship of the different categories of medicines. This incorporation of patient information suggests that patient‐centric FM thinking can potentially guide more coordinated supply chains and inventories, and better‐informed operation and maintenance decisions on the basis of the anticipated length of stay for admitted inpatients. Author Contributions: X.X.: Conceptualization, Writing—Original Draft; Z.F.: Investigation, Visu‐ alization, Writing—Original Draft; L.C.: Methodology, Writing—Review and Editing, Supervision, Funding acquisition; Q.L.: Methodology, Writing—Review and Editing, Supervision, Project ad‐ ministration, Funding acquisition; T.T.: Writing—Original Draft; Z.Y.: Resources, Data Curation; M.P.: Resources, Supervision. All authors have read and agreed to the published version of the manuscript. Funding: The work described in this paper was supported by the grant from the UCL Global En‐ gagement Strategic Partner Funds and UCL Centre for Blockchain Technologies (CBT). Institutional Review Board Statement: The study was conducted in accordance with the Declara‐ tion of Helsinki, and approved by the Ethics Committee of University College London (protocol code 2021‐Stf‐QL‐002 and date of approval: 02/06/2021). Data Availability Statement: Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Conflicts of Interest: The authors declare no conflict of interest. References 1. Ford, G.; Compton, M.; Millett, G.; Tzortzis, A. The role of digital disruption in healthcare service innovation. In Service Business Model Innovation in Healthcare and Hospital Management; Springer: Cham, Switzerland, 2017; pp. 57–70. 2. Wiens, J.; Guttag, J.; Horvitz, E. A study in transfer learning: Leveraging data from multiple hospitals to enhance hospital‐ specific predictions. J. Am. Med. Inform. Assoc. 2014, 21, 699–706. 3. Diez, K.; Lennerts, K. A process‐oriented analysis of facility management services in hospitals as a basis for strategic planning. J. Facil. Manag. 2009, 7, 52–60. 4. Mosadeghrad, A.M. Factors influencing healthcare service quality. Int. J. Health Policy Manag. 2014, 3, 77. 5. Pheng, L.S.; Rui, Z. Facilities Management and Singapore’s Healthcare System. In Service Quality for Facilities Management in Hospitals; Springer: Singapore, 2016; pp. 9–23. 6. Saedi, S.; Kundakcioglu, O.E.; Henry, A.C. Mitigating the impact of drug shortages for a healthcare facility: An inventory man‐ agement approach. Eur. J. Oper. Res. 2016, 251, 107–123. Buildings 2022, 12, 888 18 of 19 7. Maestre, J.M.; Fernández, M.I.; Jurado, I. An application of economic model predictive control to inventory management in hospitals. Control. Eng. Pract. 2018, 71, 120–128. 8. Calde, S.; Goodwin, K.; Reimann, R. SHS Orcas: The first integrated information system for long‐term healthcare facility man‐ agement. In Case Studies of the CHI2002 AIGA Experience Design FORUM; Association for Computing Machinery: New York, NY, USA, 2002; pp. 2–16. 9. Lavy, S.; Fernández‐Solis, J. Complex healthcare facility management and lean construction. HERD Health Environ. Res. Des. J. 2010, 3, 3–6. 10. Lavy, S.; Shohet, I.M. A strategic integrated healthcare facility management model. Int. J. Strateg. Prop. Manag. 2007, 11, 125– 142. 11. Wang, Y.; Kung, L.; Byrd, T.A. Big data analytics: Understanding its capabilities and potential benefits for healthcare organiza‐ tions. Technol. Forecast. Soc. Change 2018, 126, 3–13. 12. Wang, Z.; Bulbul, T.; Lucas, J. A case study of BIM‐based model adaptation for healthcare facility management—Information needs analysis. In Computing in Civil Engineering; American Society of Civil Engineers: Reston, VA, USA, 2015; pp. 395–402. 13. Yousefli, Z.; Nasiri, F.; Moselhi, O. Maintenance workflow management in hospitals: An automated multi‐agent facility man‐ agement system. J. Build. Eng. 2020, 32, 101431. 14. Allen, E.P.; Muhwezi, W.W.; Henriksson, D.K.; Mbonye, A.K. Health facility management and access: A qualitative analysis of challenges to seeking healthcare for children under five in Uganda. Health Policy Plan. 2017, 32, 934–942. 15. Normalization, C. EN 15221‐1: European Standard in Facility Management–Part 1: Terms and Definitions; CEN: Brussels, Belgium, 2006. 16. Yavorskyi, A. Evaluation of Efficacy of Patient‐Oriented Measures to Control the Health Care Quality at the Level of Healthcare Facilities. Galician Med. J. 2016, 23, 38–43. 17. Coenen, C.; von Felten, D. A service‐oriented perspective of facility management. Facilities 2014, 32, 554–564, ISSN 0263‐2772. 18. Fredendall, L.D.; Craig, J.B.; Fowler, P.J.; Damali, U. Barriers to swift, even flow in the internal supply chain of perioperative surgical services department: A case study. Decis. Sci. 2009, 40, 327–349. 19. Chen, I.J.; Paulraj, A. Towards a theory of supply chain management: The constructs and measurements. J. Oper. Manag. 2004, 22, 119–150. 20. Kannan, V.R.; Tan, K.C. Just in time, total quality management, and supply chain management: Understanding their linkages and impact on business performance. Omega 2005, 33, 153–162. 21. Meijboom, B.; Schmidt‐Bakx, S.; Westert, G. Supply chain management practices for improving patient‐oriented care. Supply Chain. Manag. 2011, 16, 166–175. https://doi.org/10.1108/13598541111127155. 22. Jovanović, A.; Klimek, P.; Renn, O.; Schneider, R.; Øien, K.; Brown, J.; Chhantyal, P. Assessing resilience of healthcare infra‐ structure exposed to COVID‐19: Emerging risks, resilience indicators, interdependencies and international standards. Environ. Syst. Decis. 2020, 40, 252–286. 23. Sharma, A.; Borah, S.B.; Moses, A.C. Responses to COVID‐19: The role of governance, healthcare infrastructure, and learning from past pandemics. J. Bus. Res. 2021, 122, 597–607. 24. Yu, D.E.C.; Razon, L.F.; Tan, R.R. Can global pharmaceutical supply chains scale up sustainably for the COVID‐19 crisis? Resour. Conserv. Recycl. 2020, 159, 104868. 25. Geesteranus, P.M.; Mańkowski, C. Healthcare logistics: An integral, process‐oriented approach. Transp. Econ. Logist. 2017, 71, 35–47. 26. Ageron, B.; Benzidia, S.; Bourlakis, M. Healthcare logistics and supply chain–issues and future challenges. Supply Chain. Forum Int. J. 2018, 19, 1–3. 27. Mathur, B.; Gupta, S.; Meena, M.L.; Dangayach, G.S. Healthcare supply chain management: Literature review and some issues. J. Adv. Manag. Res. 2018, 15, 265–287. https://doi.org/10.1108/JAMR‐09‐2017‐0090. 28. Dash, S.; Shakyawar, S.K.; Sharma, M.; Kaushik, S. Big data in healthcare: Management, analysis and future prospects. J. Big Data 2019, 6, 1–25. 29. Kumar, A.; Rahman, S. RFID‐enabled process reengineering of closed‐loop supply chains in the healthcare industry of Singa‐ pore. J. Clean. Prod. 2014, 85, 382–394. 30. Romero, A.; Lefebvre, E. Combining barcodes and RFID in a hybrid solution to improve hospital pharmacy logistics processes. Int. J. Inf. Technol. Manag. 2015, 14, 97–123. 31. Böhme, T.; Williams, S.J.; Childerhouse, P.; Deakins, E.; Towill, D. Causes, effects and mitigation of unreliable healthcare sup‐ plies. Prod. Plan. Control. 2016, 27, 249–262. 32. Parnaby, J.; Towill, D.R. Engineering cellular organisation and operation for effective healthcare delivery supply chains. Int. J. Logist. Manag. 2009, 20, 5–29. https://doi.org/10.1108/09574090910954828. 33. Gebicki, M.; Mooney, E.; Chen, S.J.G.; Mazur, L.M. Evaluation of hospital medication inventory policies. Health Care Manag. Sci. 2014, 17, 215–229. 34. Jones, G.L.; Peter, Z.; Rutter, K.A.; Somauroo, A. Promoting an Overdue Digital Transformation in Healthcare. Ariel 2019. Avail‐ able online: https://www.mckinsey.com/~/media/McKinsey/Industries/Healthcare%20Systems%20and%20Services/Our%20In‐ sights/Promoting%20an%20overdue%20digital%20transformation%20in%20healthcare/Promoting‐an‐overdue‐digital‐trans‐ formation‐in‐healthcare.pdf (accessed on 12 December 2021). Buildings 2022, 12, 888 19 of 19 35. Qin, G.; Li, Q.; Deng, X. Cluster computing in drug logistic monitoring and management. In International Conference on Cooper‐ ative Design, Visualization and Engineering; Springer: Berlin/Heidelberg, Germany, 2008; pp. 307–310. 36. Carrus, P.P.; Marras, F.; Musso, M.; Pinna, R. The impact of digitalization in the drugs logistics and clinical process: An Italian case study. In Proceedings of the XXVIII Sinergie Annual Conference Management in a Digital World. Decisions, Production, Communication, Udine, Italy, 9–10 June 2016. 37. Chikumba, P.A. Application of geographic information system (GIS) in drug logistics management information system (LMIS) at district level in Malawi: Opportunities and challenges. In Proceedings of the International Conference on e‐Infrastructure and e‐ Services for Developing Countries, Maputo, Mozambique, 3–4 December 2009; Springer: Berlin/Heidelberg, Germany, 2009; pp. 105– 115. 38. Baek, S.I.; Shin, H.R.; Park, M.H.; Doo, I.C. A study on the management of drug logistics using beacon technology. Adv. Sci. Lett. 2018, 24, 1979–1985. 39. Güner, S.; Rathnayake, D.; Ahmadi, N.B.; Kim, B. Using Unmanned Aerial Vehicles—Drones as a Logistic Method in Pharma‐ ceutical Industry in Germany. Aviation 2017, 1, 1–11. 40. Casado‐Vara, R.; González‐Briones, A.; Prieto, J.; Corchado, J.M. Smart contract for monitoring and control of logistics activities: Pharmaceutical utilities case study. In Proceedings of the 13th International Conference on Soft Computing Models in Industrial and Environmental Applications, San Sebastian, Spain, 6–8 June 2018; Springer: Cham, Switzerland, 2018; pp. 509–517. 41. Ershadi, M.M.; Ershadi, M.S. Logistic planning for pharmaceutical supply chain using multi‐objective optimization model. Int. J. Pharm. Healthc. Mark. 2022, 16, 75–100. https://doi.org/10.1108/IJPHM‐01‐2021‐0004. 42. Du, M.; Luo, J.; Wang, S.; Liu, S. Genetic algorithm combined with BP neural network in hospital drug inventory management system. Neural Comput. Appl. 2020, 32, 1981–1994. 43. Nikzad, E.; Bashiri, M.; Oliveira, F. Two‐stage stochastic programming approach for the medical drug inventory routing prob‐ lem under uncertainty. Comput. Ind. Eng. 2019, 128, 358–370. 44. Aagja, J.P.; Garg, R. Measuring perceived service quality for public hospitals (PubHosQual) in the Indian context. Int. J. Pharm. Healthc. Mark. 2010, 4, 60–83. https://doi.org/10.1108/17506121011036033. 45. Camilleri, D.; O’Callaghan, M. Comparing public and private hospital care service quality. Int. J. Health Care Qual. Assur. 1998, 11, 127–133. https://doi.org/10.1108/09526869810216052. 46. Jabnoun, N.; Chaker, M. Comparing the quality of private and public hospitals. Manag. Serv. Qual. Int. J. 2003, 13, 290–299. 47. Vandamme, R.; Leunis, J. Development of a multiple‐item scale for measuring hospital service quality. Int. J. Serv. Ind. Manag. 1993, 4, 30–49. 48. Fernández, M.I.; Chanfreut, P.; Jurado, I.; Maestre, J.M. A Data‐Based Model Predictive Decision Support System for Inventory Management in Hospitals. IEEE J. Biomed. Health Inform. 2020, 25, 2227–2236. 49. Li, N.; Chiang, F.; Down, D.G.; Heddle, N.M. A decision integration strategy for short‐term demand forecasting and ordering for red blood cell components. Oper. Res. Health Care 2021, 29, 100290.
Buildings – Multidisciplinary Digital Publishing Institute
Published: Jun 23, 2022
Keywords: patient-oriented; facility management; logistics management; pharmaceutical services; hospital environments
Access the full text.
Sign up today, get DeepDyve free for 14 days.