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Dynamic Optimization of Caregiver Schedules Based on Vital Sign Streams

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DOI: 10.4236/etsn.2013.22006    4,116 Downloads   6,610 Views  


Hospital facilities use a collection of heterogeneous devices, produced by many different vendors, to monitor the state of patient vital signs. The limited interoperability of current devices makes it difficult to synthesize multivariate monitoring data into a unified array of real-time information regarding the patients state. Without an infrastructure for the integrated evaluation, display, and storage of vital sign data, one cannot adequately ensure that the assignment of caregivers to patients reflects the relative urgency of patient needs. This is an especially serious issue in critical care units (CCUs). We present a formal mathematical model of an operational critical care unit, together with metrics for evaluating the systematic impact of caregiver scheduling decisions on patient care. The model is rich enough to capture the essential features of device and patient diversity, and so enables us to test the hypothesis that integration of vital sign data could realistically yield a significant positive impact on the efficacy of critical care delivery outcome. To test the hypothesis, we employ the model within a computer simulation. The simulation enables us to compare the current scheduling processes in widespread use within CCUs, against a new scheduling algorithm that makes use of an integrated array of patient information collected by an (anticipated) vital sign data integration infrastructure. The simulation study provides clear evidence that such an infrastructure reduces risk to patients and lowers operational costs, and in so doing reveals the inherent costs of medical device non-interoperability.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

M. Saad and B. Khan, "Dynamic Optimization of Caregiver Schedules Based on Vital Sign Streams," E-Health Telecommunication Systems and Networks, Vol. 2 No. 2, 2013, pp. 36-47. doi: 10.4236/etsn.2013.22006.


[1] J. M. Corrigan, L. T. Kohn and M. S. Donaldso, “To Err Is Human. Building a Safer Health System,” Institute of Medicine, 2000.
[2] S. Loughran, “In-Hospital Deaths from Medical Errors at 195,000 per Year,” Health Grades Study Finds, Health-Grades, 2004.
[3] The Joint Commission, “Preventing Ventilator-Related Deaths and Injuries,” Sentinel Event Alert of the Joint Commission, February 2002.
[4] M. McManus, M. Long, A. Cooper and E. Litvak, “Queuing Theory Accurately Models the Need for Critical Care Resources,” Anesthesiology, Vol. 100, No. 5, 2004, pp. 1271-1276. doi:10.1097/00000542-200405000-00032
[5] A. Zai, K. Farr, R. Grant, E. Mort, T. Ferris and H. Chueh, “Queuing Theory to Guide the Implementation of a Heart Failure Inpatient Registry Program,” Journal of American Medical Information Association, Vol. 16, No. 4, 2009, pp. 516-523. doi:10.1197/jamia.M2977
[6] P. Mathews, L. Drumheller and J. Carlow, “Respiratory Care Manpower Issues,” Critical Care Medicine, Vol. 34, No. 3, 2006, pp. 32-45. doi:10.1097/01.CCM.0000203103.11863.BC
[7] S. Gallivan, M. Utley, T. Treasure and O. Valencia, “Booked Inpatient Admissions and Hospital Capacity: Mathematical Modelling Study,” BMJ, Vol. 324, 2002, pp. 280-282. doi:10.1136/bmj.324.7332.280
[8] A. Shahani, S. Ridley and M. Nielsen, “Modelling Patient Flows as an Aid to Decision Making for Critical Care Capacities and Organization,” Anaesthesia, Vol. 63, No. 10, 2008, pp. 1074-1080. doi:10.1111/j.1365-2044.2008.05577.x
[9] G. Baskaran, A. Bargiela and R. Qu, “Hierarchical Method for Nurse Rostering Based on Granular Pre-Processing of Constraints,” Proceedings of the 23rd EUROPEAN Conference on Modelling and Simulation, Madrid, 9-12 June 2009, pp. 855-861.
[10] R. Ratnayaka, Z. Wang, S. Anamalamudi and S. Cheng, “Enhanced greedy optimization algorithm with data warehousing for automated nurse scheduling system,” E-Health Telecommunication Systems and Networks, Vol. 2, 2012, pp. 43-48.
[11] S. Kundu, M. Mahato, B. Mahanty and S. Acharyya, “Comparative performance of simulated annealing and genetic algorithm in solving nurse scheduling problem,” In Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong, 19-21 March 2008, p. 96.
[12] “JBI Clinical Online Network of Evidence for Care and Therapeutics,” The-Joanna-Briggs-Institute, Vital signs, Vol. 3, No. 3, 1999, pp. 1-6.
[13] K. M. Hillman, P. J. Bristow, T. Chey, K. Daffurn, T. Jacques, S. L. Norman, G. F. Bishop and G. Simmons, “Antecedents to Hospital Deaths,” Internal Medicine Journal, Vol. 31, No. 6, 2001, pp. 343-348. doi:10.1046/j.1445-5994.2001.00077.x
[14] J. H. Van Oostrom, C. Gravenstein and J. S. Gravenstein, “Acceptable Ranges for Vital Signs during General Anesthesia,” Journal of Clinical Monitoring and Computing, Vol. 9, 1993, pp. 321-325.
[15] Medical Equipment Manufacturers Directory, DRE-Inc. 2010.
[16] C. P. Friedman, “A Fundamental Theorem of Biomedical Informatics,” Journal of the American Medical Informatics Association, Vol. 16, No. 2, 2009, pp.169-170. doi:10.1197/jamia.M3092
[17] Y. B. Kim, M. Kim and Y. J. Lee, “Cosmos: A Middleware Platform for Sensor Networks and a u-Healthcare Service,” Proceedings of the 2008 ACM symposium on Applied computing, New York, 2008, pp. 512-513.
[18] P. Fuhrer and D. Guinard, “Building a Smart Hospital Using RFID Technologies,” European Conference on eHealth, 2006, pp. 131-142.
[19] S.-W. Wang, W.-H. Chen, C.-S. Ong, Li Liu, and Yun-Wen Chuang, “RFID Application in Hospitals: A Case Study on a Demonstration RFID Project in a Taiwan Hospital,” Proceedings of the 39th Annual Hawaii International Conference on System Sciences, Washington DC, 2006, p. 184.1.
[20] S. Manfredi, “Performance Evaluation of Healthcare Monitoring System over Heterogeneous Wireless Networks,” E-Health Telecommunication Systems and Networks, Vol. 1, pp. 27-36, 2012. doi:org/10.4236/etsn.2012.13005
[21] S. Czosnyka, M. Richards, H. K. Whitfield, P. Pickard, and J. Piechnik, “Cerebral Venous Blood Outflow: A Theoretical Model Based on Laboratory Simulation,” Informa Healthcare, Vol. 49, No. 5, 2001. pp. 1214-1223.
[22] P. W. Lai, “Model of Injury Severity Allowing for Different Gradings of Severity: Some Applications Using the British Road Accident Data,” Accident Analysis & Prevention, Vol. 12, No. 3, 1980, pp. 221-239. doi:10.1016/0001-4575(80)90023-8
[23] J. J. Crisco and M. M. Panjabi, “Euler stability of the human ligamentous lumbar spine. Part I: Theory,” Clinical Biomechanics, Vol. 7, No. 1, 1992, pp. 19-26. doi:10.1016/0268-0033(92)90003-M
[24] S. Albers and S. Leonardi, “On-Line Algorithms,” Association of Computing Machinery Computing Surveys, 1999, p. 4.
[25] M. Manasse, L. McGeoch and D. Sleator, “Competitive Algorithms for On-Line Problems,” Proceedings of the Twentieth Annual ACM Symposium on Theory of Computing, Chicago, 2-4 May 1988, pp. 322-333. doi:10.1145/62212.62243

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