Towards Evaluating Proactive and Reactive Approaches on Reorganizing Human Resources in IoT-Based Smart Hospitals

Hospitals play an important role on ensuring a proper treatment of human health. One of the problems to be faced is the increasingly overcrowded patients care queues, who end up waiting for longer times without proper treatment to their health problems. The allocation of health professionals in hospital environments is not able to adapt to the demands of patients. There are times when underused rooms have idle professionals, and overused rooms have fewer professionals than necessary. Previous works have not solved this problem since they focus on understanding the evolution of doctor supply and patient demand, as to better adjust one to the other. However, they have not proposed concrete solutions for that regarding techniques for better allocating available human resources. Moreover, elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Based on this background, we introduce Elastic allocation of human resources in Healthcare environments (ElHealth) an IoT-focused model able to monitor patient usage of hospital rooms and adapt these rooms for patients demand. Using reactive and proactive elasticity approaches, ElHealth identifies when a room will have a demand that exceeds the capacity of care, and proposes actions to move human resources to adapt to patient demand. Our main contribution is the definition of Human Resources IoT-based Elasticity (i.e., an extension of the concept of resource elasticity in Cloud Computing to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patient demand). Another contribution is a cost–benefit analysis for the use of reactive and predictive strategies on human resources reorganization. ElHealth was simulated on a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.4% and 96.73% in reactive and proactive approaches, respectively.

[1]  R. Scheffler,et al.  Global Health Workforce Labor Market Projections for 2030 , 2016, Human Resources for Health.

[2]  Philippe Merle,et al.  Coordinating Vertical Elasticity of both Containers and Virtual Machines , 2018, CLOSER.

[3]  Wlodek Kulesza,et al.  Customization of UWB 3D-RTLS Based on the New Uncertainty Model of the AoA Ranging Technique , 2017, Sensors.

[4]  Eduardo Huedo,et al.  Efficient resource provisioning for elastic Cloud services based on machine learning techniques , 2019, Journal of Cloud Computing.

[5]  T. Ishikawa,et al.  Forecasting the regional distribution and sufficiency of physicians in Japan with a coupled system dynamics—geographic information system model , 2017, Human Resources for Health.

[6]  K. Sreekumar,et al.  A review and analysis of machine learning and statistical approaches for prediction , 2017, 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT).

[7]  Cristiano André da Costa,et al.  Joint‐analysis of performance and energy consumption when enabling cloud elasticity for synchronous HPC applications , 2016, Concurr. Comput. Pract. Exp..

[8]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[9]  Aniruddha S. Gokhale,et al.  Performance Interference-Aware Vertical Elasticity for Cloud-Hosted Latency-Sensitive Applications , 2018, 2018 IEEE 11th International Conference on Cloud Computing (CLOUD).

[10]  Cristiano André da Costa,et al.  AutoElastic: Automatic Resource Elasticity for High Performance Applications in the Cloud , 2016, IEEE Transactions on Cloud Computing.

[11]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[12]  Dimosthenis Kyriazis,et al.  IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices , 2019, Sensors.

[13]  Walid A. Hanafy,et al.  A New Infrastructure Elasticity Control Algorithm for Containerized Cloud , 2019, IEEE Access.

[14]  Sylvester Olubolu Orimaye,et al.  Predicting proximity with ambient mobile sensors for non-invasive health diagnostics , 2015, 2015 IEEE 12th Malaysia International Conference on Communications (MICC).

[15]  Qusay Idrees Sarhan Internet of things: a survey of challenges and issues , 2018, IoT 2018.

[16]  Rajkumar Buyya,et al.  Brownout Approach for Adaptive Management of Resources and Applications in Cloud Computing Systems , 2019, ACM Comput. Surv..

[17]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[18]  Jorge Luis Victória Barbosa,et al.  ElCity: An Elastic Multilevel Energy Saving Model for Smart Cities , 2018, IEEE Transactions on Sustainable Computing.

[19]  Arun Kejariwal,et al.  Techniques for Optimizing Cloud Footprint , 2013, 2013 IEEE International Conference on Cloud Engineering (IC2E).

[20]  Bjorn P. Berg,et al.  Improving Clinic Operational Efficiency and Utilization with RTLS , 2019, Journal of Medical Systems.

[21]  Peter R. Winters,et al.  Forecasting Sales by Exponentially Weighted Moving Averages , 1960 .

[22]  K. Anandakumar,et al.  A comprehensive review on usage of Internet of Things (IoT) in healthcare system , 2015, 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT).

[23]  Divneet Singh Kapoor,et al.  Create Your Own Internet of Things: A survey of IoT platforms. , 2017, IEEE Consumer Electronics Magazine.

[24]  Marco Aurélio Stelmar Netto,et al.  Evaluating Auto-scaling Strategies for Cloud Computing Environments , 2014, 2014 IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems.

[25]  Oliver Sinnen,et al.  List-Scheduling versus Cluster-Scheduling , 2018, IEEE Transactions on Parallel and Distributed Systems.

[26]  Victor I. Chang,et al.  Towards Enabling Live Thresholding as Utility to Manage Elastic Master-Slave Applications in the Cloud , 2017, Journal of Grid Computing.

[27]  Khaled Salah,et al.  Impact of CPU Utilization Thresholds and Scaling Size on Autoscaling Cloud Resources , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[28]  Faiez Zalila,et al.  Model-Driven Elasticity Management with OCCI , 2019, IEEE Transactions on Cloud Computing.

[29]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[30]  Cristiano André da Costa,et al.  Enhancing performance of IoT applications with load prediction and cloud elasticity , 2020, Future Gener. Comput. Syst..

[31]  Jaakko Hollmén,et al.  Resource Frequency Prediction in Healthcare: Machine Learning Approach , 2016, 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS).

[32]  Cristiano André da Costa,et al.  Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards , 2018, Artif. Intell. Medicine.

[33]  J. L. Hodges,et al.  Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties , 1989 .

[34]  Raymond Bond,et al.  Using Data Mining to Predict Hospital Admissions From the Emergency Department , 2018, IEEE Access.

[35]  P. Whittle Hypothesis testing in time series analysis , 1954 .

[36]  M. Grypdonck,et al.  Health professionals’ dealing with hope in palliative patients with cancer, an explorative qualitative research , 2018, European journal of cancer care.

[37]  Dhananjay Singh,et al.  Elastic-RAN: An adaptable multi-level elasticity model for Cloud Radio Access Networks , 2019, Comput. Commun..

[38]  Peng Song,et al.  Sensitive time series prediction using extreme learning machine , 2019, Int. J. Mach. Learn. Cybern..

[39]  Alexandru Butean,et al.  Auxilum Medicine: A Cloud Based Platform for Real-Time Monitoring Medical Devices , 2015, 2015 20th International Conference on Control Systems and Computer Science.

[40]  Maged N Kamel Boulos,et al.  Real-time locating systems (RTLS) in healthcare: a condensed primer. , 2012, International journal of health geographics.

[41]  Francisco Vázquez,et al.  Simulation Tool for the Analysis of Cooperative Localization Algorithms for Wireless Sensor Networks , 2019, Sensors.

[42]  A. Lawson,et al.  Neighborhood level risk factors for type 1 diabetes in youth: the SEARCH case-control study , 2012, International Journal of Health Geographics.

[43]  Raffaela Mirandola,et al.  Simulation of Techniques to Improve the Utilization of Cloud Elasticity in Workload-aware Adaptive Software , 2016, ICPE Companion.

[44]  Philippe O. A. Navaux,et al.  A lightweight plug-and-play elasticity service for self-organizing resource provisioning on parallel applications , 2018, Future Gener. Comput. Syst..

[45]  Mojtaba Vahidi-Asl,et al.  DMP-IOT: A distributed movement prediction scheme for IOT health-care applications , 2017, Comput. Electr. Eng..

[46]  Kevin Lee,et al.  How a consumer can measure elasticity for cloud platforms , 2012, ICPE '12.

[47]  R. Righi,et al.  Personal Health Records: A Systematic Literature Review , 2017, Journal of medical Internet research.

[48]  Philippe Merle,et al.  Elasticity in Cloud Computing: State of the Art and Research Challenges , 2018, IEEE Transactions on Services Computing.

[49]  Cristiano André da Costa,et al.  Towards providing middleware-level proactive resource reorganisation for elastic HPC applications in the cloud , 2019, Int. J. Grid Util. Comput..

[50]  D. Cox The Regression Analysis of Binary Sequences , 1958 .