Metrics for eHealth services improvement

Identification of appropriate performance measures in healthcare is fundamental for judging quality of service in any healthcare organisation. However, the dynamic nature of the healthcare context and complexity of stakeholder requirements have resulted in many difficulties in deciding appropriateness of performance factors to measure outcomes. Yet another facet that hinders achievement of successful performance measuring approaches is the unavailability of systematic guidance to identify the relationship between different types of performance measures. Further, it is important to analyse this relationship by using existing electronic data sets which will ensure the data quality in determining the healthcare service quality. This is an ongoing research attempting to establish a Return on Investment (ROI) model that could facilitate performance measurement of eHealth service deployment while overcoming the aforementioned deficiencies. The systematic guidance of deciding key performance factors considering different healthcare value perspectives in order to establish ROI metrics for the healthcare context, specifically in ICU clinical settings, have been introduced in this paper. It can be served as a theoretical basis in ensuring data quality in eHealth data sources. The advantage of the proposed guidance is in extracting appropriate key performance indicators for measuring outcomes of ICU clinical settings in terms of available process indicators while the relevant data for these indicators could be retrieved from the data warehouse.

[1]  Nir Menachemi,et al.  Reviewing the Benefits and Costs of Electronic Health Records and Associated Patient Safety Technologies , 2006, Journal of Medical Systems.

[2]  Philip Troy,et al.  Using simulation to determine the need for ICU beds for surgery patients. , 2009, Surgery.

[3]  Sotiris Pavlopoulos,et al.  Using key performance indicators as knowledge-management tools at a regional health-care authority level , 2005, IEEE Transactions on Information Technology in Biomedicine.

[4]  G. Westert,et al.  Quality measurement at intensive care units: which indicators should we use? , 2007, Journal of critical care.

[5]  M. Müller,et al.  Simulation in the intensive care setting. , 2015, Best practice & research. Clinical anaesthesiology.

[6]  K. Wernecke,et al.  Key Performance Indicators in Intensive Care Medicine. A Retrospective Matched Cohort Study , 2009, The Journal of international medical research.

[7]  G. M. Koole,et al.  Modeling the emergency cardiac in-patient flow: an application of queuing theory , 2007, Health care management science.

[8]  Peter J Pronovost,et al.  Qualitative review of intensive care unit quality indicators. , 2002, Journal of critical care.

[9]  Kim Seung-Chul,et al.  Flexible bed allocation and performance in the intensive care unit , 2000 .

[10]  D. Nerenz Performance Measures for Health Care Systems , 2001 .

[11]  Michel Bierlaire,et al.  An analytic finite capacity queueing network model capturing the propagation of congestion and blocking , 2009, Eur. J. Oper. Res..

[12]  Louis Raymond,et al.  Researching performance measurement systems , 2008 .

[13]  D. Eddy,et al.  Enhancing performance measurement: NCQA's road map for a health information framework. National Committee for Quality Assurance. , 1999, JAMA.

[14]  Pradip Kumar Ray,et al.  Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections , 2014, Comput. Ind. Eng..

[15]  Eric T. Bradlow,et al.  Does hospital performance on process measures directly measure high quality care or is it a marker of unmeasured care? , 2007, Health services research.

[16]  Rourke Aj Evaluating the quality of medical care. , 1957, Hospital progress.

[17]  W. C. Benton,et al.  Performance measurement criteria in health care organizations: Review and future research directions , 1996 .

[18]  N. Menachemi,et al.  Hospital Information Technology and Positive Financial Performance: A Different Approach to Finding an ROI , 2006, Journal of healthcare management / American College of Healthcare Executives.

[19]  Eric Howell,et al.  Hospitalist bed management effecting throughput from the emergency department to the intensive care unit. , 2010, Journal of critical care.

[20]  Paul Robert Harper,et al.  Modelling for the planning and management of bed capacities in hospitals , 2002, J. Oper. Res. Soc..

[21]  Sally C. Brailsford,et al.  OR in healthcare: A European perspective , 2011, Eur. J. Oper. Res..

[22]  D. Osoba,et al.  Measuring health-related quality of life. , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[23]  Kathy Lee Simunich,et al.  How to determine future EHR ROI. Agent-based modeling and simulation offers a new alternative to traditional techniques. , 2008, Journal of healthcare information management : JHIM.

[24]  Mohamud Daya,et al.  Effect of increased ICU capacity on emergency department length of stay and ambulance diversion. , 2005, Annals of emergency medicine.

[25]  P. Shekelle,et al.  Defining and measuring quality of care: a perspective from US researchers. , 2000, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[26]  Andre Kushniruk,et al.  A Framework for Usable and Effective Clinical Decision Support: Experience from the iCPR Randomized Clinical Trial , 2015, EGEMS.