Factors related to monitoring during admission of acute patients

Understanding the use of patient monitoring systems in emergency and acute facilities may help to identify reasons for failure to identify risk patients in these settings. Hence, we investigate factors related to the utilization of automated monitoring for patients admitted to an acute admission unit by introducing monitor load as the proportion between monitored time and length of stay. A cohort study of patients admitted and registered to patient monitors in the period from 10/10/2013 to 1/10/2014 at the acute admission unit of Odense University Hospital in Denmark. Admissions with at least one measurement were analyzed using quantile regression by looking at the impact of distance from nursing office, number of concurrent patients, wing type (medical/surgical), age, sex, comorbidities, and severity conditioned on how much patients were monitored during their admissions. We registered 11,848 admissions, of which we were able to link patient monitor readings to 3149 (26.6 %) with 50 % being monitored <1.4 % of total admission time. Distance from nursing office had little influence on patients monitored <10 % of their admission time. But for other patients, being positioned further away from the office reduced the level of monitoring. Higher levels of severity were related to higher degrees of monitoring, but being admitted to the surgical wing reduce how much patients were monitored, and periods with many concurrent patients lead to a small increase in monitoring. We found a significant variation concerning how much patients were monitored during admission to an acute admission unit. Our results point to potential patient safety improvements in clinical procedures, and advocate an awareness of how patient monitoring systems are utilized.

[1]  Inga Adams-Pizarro,et al.  "Identifying the hospitalised patient in crisis"--a consensus conference on the afferent limb of rapid response systems. , 2010, Resuscitation.

[2]  Vivian Yi-Ju Chen,et al.  Using quantile regression to examine the effects of inequality across the mortality distribution in the U.S. counties. , 2012, Social science & medicine.

[3]  Marc Berg,et al.  Overriding of drug safety alerts in computerized physician order entry. , 2006, Journal of the American Medical Informatics Association : JAMIA.

[4]  Diane L. Seger,et al.  A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems , 2010, J. Am. Medical Informatics Assoc..

[5]  Charlene R. Weir,et al.  Intensive care unit nurses' information needs and recommendations for integrated displays to improve nurses' situation awareness , 2012, J. Am. Medical Informatics Assoc..

[6]  Jesper Hallas,et al.  Risk scoring systems for adults admitted to the emergency department: a systematic review , 2010, Scandinavian journal of trauma, resuscitation and emergency medicine.

[7]  F Cerne,et al.  Redefining the hospital. , 1994, Trustee : the journal for hospital governing boards.

[8]  Andrew Emmanuel,et al.  Who will be sicker in the morning? Changes in the Simple Clinical Score the day after admission and the subsequent outcomes of acutely ill unselected medical patients. , 2011, European journal of internal medicine.

[9]  Paul E. Schmidt,et al.  Review and performance evaluation of aggregate weighted 'track and trigger' systems. , 2008, Resuscitation.

[10]  D. Prytherch,et al.  An overview of the afferent limb , 2011 .

[11]  Hester Vermeulen,et al.  Clinical relevance of routinely measured vital signs in hospitalized patients: a systematic review. , 2014, Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing.

[12]  Alex Taylor,et al.  Factors affecting response to national early warning score (NEWS). , 2015, Resuscitation.

[13]  C. Padula,et al.  Knowing 'something is not right' is beyond intuition: development of a clinical algorithm to enhance surveillance and assist nurses to organise and communicate clinical findings. , 2015, Journal of clinical nursing.

[14]  Elsebeth Lynge,et al.  The Danish National Patient Register , 2011, Scandinavian journal of public health.

[15]  David R Prytherch,et al.  A review, and performance evaluation, of single-parameter "track and trigger" systems. , 2008, Resuscitation.

[16]  Uffe Kock Wiil,et al.  Identifying patients at risk of deterioration in the Joint Emergency Department , 2015, Cognition, Technology & Work.

[17]  Paul Meredith,et al.  Patterns in the recording of vital signs and early warning scores: compliance with a clinical escalation protocol , 2013, BMJ quality & safety.

[18]  M. Odell,et al.  Detection and management of the deteriorating ward patient: an evaluation of nursing practice. , 2015, Journal of clinical nursing.

[19]  Annmarie Touborg Lassen,et al.  Prognosis and Risk Factors for Deterioration in Patients Admitted to a Medical Emergency Department , 2014, PloS one.

[20]  R. Koenker,et al.  Regression Quantiles , 2007 .

[21]  Lionel Tarassenko,et al.  Testing of Wearable Monitors in a Real-World Hospital Environment: What Lessons Can Be Learnt? , 2012, 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks.

[22]  M. Hravnak,et al.  Causes of Failure to Rescue , 2011 .

[23]  D. Harrison,et al.  Systematic review and evaluation of physiological track and trigger warning systems for identifying at-risk patients on the ward , 2007, Intensive Care Medicine.

[24]  Mary A. Dolansky,et al.  The factors that affect the frequency of vital sign monitoring in the emergency department. , 2014, Journal of emergency nursing: JEN : official publication of the Emergency Department Nurses Association.

[25]  G. McNeill,et al.  Do either early warning systems or emergency response teams improve hospital patient survival? A systematic review. , 2013, Resuscitation.

[26]  H. Simpson,et al.  Recording of vital signs in a district general hospital emergency department , 2008, Emergency Medicine Journal.

[27]  M. Odell,et al.  Nurses' role in detecting deterioration in ward patients: systematic literature review. , 2009, Journal of advanced nursing.

[28]  C. Mackenzie,et al.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.

[29]  Jesper Hallas,et al.  Nurses and Physicians in a Medical Admission Unit Can Accurately Predict Mortality of Acutely Admitted Patients: A Prospective Cohort Study , 2014, PloS one.

[30]  L. Tarassenko,et al.  The digital patient. , 2013, Clinical medicine.

[31]  Henrik Toft Sørensen,et al.  The Danish Civil Registration System as a tool in epidemiology , 2014, European Journal of Epidemiology.

[32]  Alain F. Zuur,et al.  A protocol for data exploration to avoid common statistical problems , 2010 .

[33]  K. Hillman,et al.  Redefining in-hospital resuscitation: the concept of the medical emergency team. , 2001, Resuscitation.

[34]  L. Tarassenko,et al.  A randomised controlled trial of the effect of continuous electronic physiological monitoring on the adverse event rate in high risk medical and surgical patients , 2006, Anaesthesia.

[35]  Mohammed A Mohammed,et al.  Impact of introducing an electronic physiological surveillance system on hospital mortality , 2015, BMJ quality & safety.

[36]  Jessica A. R. Logan,et al.  Quantile regression in the study of developmental sciences. , 2014, Child development.

[37]  R. Koenker,et al.  Robust Tests for Heteroscedasticity Based on Regression Quantiles , 1982 .

[38]  C Wagner,et al.  Adverse events and potentially preventable deaths in Dutch hospitals: results of a retrospective patient record review study , 2002, Quality & Safety in Health Care.

[39]  H. Rutberg,et al.  Characterisations of adverse events detected in a university hospital: a 4-year study using the Global Trigger Tool method , 2014, BMJ Open.

[40]  D. Bates,et al.  Finding patients before they crash: the next major opportunity to improve patient safety , 2014, BMJ quality & safety.