Recording signs of deterioration in acute patients: The documentation of vital signs within electronic health records in patients who suffered in-hospital cardiac arrest

Summary Background: Computerized clinical decision support and automation of warnings have been advocated to assist clinicians in detecting patients at risk of physiological instability. To provide reliable support such systems are dependent on high-quality vital sign data. Data quality depends on how, when and why the data is captured and/or documented. Objectives: This study aims to describe the effects on data quality of vital signs by three different types of documentation practices in five Swedish emergency hospitals, and to assess data fitness for calculating warning and triage scores. The study also provides reference data on triage vital signs in Swedish emergency care. Methods: We extracted a dataset including vital signs, demographic and administrative data from emergency care visits (n=335027) at five Swedish emergency hospitals during 2013 using either completely paper-based, completely electronic or mixed documentation practices. Descriptive statistics were used to assess fitness for use in emergency care decision support systems aiming to calculate warning and triage scores, and data quality was described in three categories: currency, completeness and correctness. To estimate correctness, two further categories –plausibility and concordance –were used. Results: The study showed an acceptable correctness of the registered vital signs irrespectively of the type of documentation practice. Completeness was high in sites where registrations were routinely entered into the Electronic Health Record (EHR). The currency was only acceptable in sites with a completely electronic documentation practice. Conclusion: Although vital signs that were recorded in completely electronic documentation practices showed plausible results regarding correctness, completeness and currency, the study concludes that vital signs documented in Swedish emergency care EHRs cannot generally be considered fit for use for calculation of triage and warning scores. Low completeness and currency were found if the documentation was not completely electronic. Citation: Skyttberg N, Chen R, Blomqvist H, Koch S. Exploring Vital Sign Data Quality in Electronic Health Records with Focus on Emergency Care Warning Scores. Appl Clin Inform 2017; 8: 880–892 https://doi.org/10.4338/ACI-2017-05-RA-0075

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Vital sign documentation in electronic records: The development of workarounds

Health Informatics Journal ◽

10.1177/1460458216663024 ◽

2016 ◽

Vol 24 (2) ◽

pp. 206-215 ◽

Cited By ~ 6

Author(s):

Jean E Stevenson ◽

Johan Israelsson ◽

Gunilla Nilsson ◽

Goran Petersson ◽

Peter A Bath

Keyword(s):

Electronic Health Records ◽

Vital Sign ◽

Healthcare Professionals ◽

Vital Signs ◽

Electronic Records ◽

Direct Impact ◽

Health Records ◽

Thematic Content Analysis ◽

Healthcare Settings ◽

Electronic Health

Workarounds are commonplace in healthcare settings. An increase in the use of electronic health records has led to an escalation of workarounds as healthcare professionals cope with systems which are inadequate for their needs. Closely related to this, the documentation of vital signs in electronic health records has been problematic. The accuracy and completeness of vital sign documentation has a direct impact on the recognition of deterioration in a patient’s condition. We examined workflow processes to identify workarounds related to vital signs in a 372-bed hospital in Sweden. In three clinical areas, a qualitative study was performed with data collected during observations and interviews and analysed through thematic content analysis. We identified paper workarounds in the form of handwritten notes and a total of eight pre-printed paper observation charts. Our results suggested that nurses created workarounds to allow a smooth workflow and ensure patients safety.

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Reconsidering what is vital about vital signs in electronic health records: Comment on Matthews et al. (2016).

American Psychologist ◽

10.1037/amp0000136 ◽

2017 ◽

Vol 72 (5) ◽

pp. 487-488 ◽

Cited By ~ 2

Author(s):

David M. Condon ◽

Sara J. Weston ◽

Patrick L. Hill

Keyword(s):

Electronic Health Records ◽

Vital Signs ◽

Health Records ◽

Electronic Health

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A Composite and Wearable Sensor Kit for Location-Aware Healthcare Monitoring and Real-Time Trauma Scoring for Survival Prediction

Applied System Innovation ◽

10.3390/asi1030035 ◽

2018 ◽

Vol 1 (3) ◽

pp. 35 ◽

Cited By ~ 1

Author(s):

Amit Walinjkar

Keyword(s):

Signal Processing ◽

Electronic Health Records ◽

Real Time ◽

Vital Signs ◽

Physiological Parameters ◽

Early Warning Score ◽

Health Records ◽

Electronic Health ◽

Trauma Score ◽

Trauma Scores

With the advances in the microfabrication of analogue front-end devices, and embedded and signal processing technology, it has now become possible to devise miniaturized health monitoring kits for non-invasive real time monitoring at any location. The current commonly available kits only measure singleton physiological parameters, and a composite analysis that covers all vital signs and trauma scores seems to be missing with these kits. The research aims at using vital signs and other physiological parameters to calculate trauma scores National Early Warning Score (NEWS), Revised Trauma Score (RTS), Trauma Score - Injury Severity Score (TRISS) and Prediction of survival (Ps), and to log the trauma event to electronic health records using standard coding schemes. The signal processing algorithms were implemented in MATLAB and could be ported to TI AM335x using MATLAB/Embedded Coder. Motion artefacts were removed using a level ‘5’ stationary wavelet transform and a ‘sym4’ wavelet, which yielded a signal-to-noise ratio of 27.83 dB. To demonstrate the operation of the device, an existing Physionet, MIMIC II Numerics dataset was used to calculate NEWS and RTS scores, and to generate the correlation and regression models for a clinical class of patients with respiratory failure and admitted to Intensive Care Unit (ICU). Parameters such as age, heart rate, Systolic Blood Pressure (SysBP), respiratory rate, and Oxygen Saturation (SpO2) as predictors to Ps, showed significant positive regressions of 93% at p < 0.001. The NEWS and RTS scores showed no significant correlation (r = 0.25, p < 0.001) amongst themselves; however, the NEWS and RTS together showed significant correlations with Ps (blunt) (r = 0.70, p < 0.001). RTS and Ps (blunt) scores showed some correlations (r = 0.63, p < 0.001), and the NEWS score showed significant correlation (r = 0.79, p < 0.001) with Ps (blunt) scores. Global Positioning System (GPS) system was built into the kit to locate the individual and to calculate the shortest path to the nearest healthcare center using the Quantum Geographical Information System (QGIS) Network Analysis tool. The physiological parameters from the sensors, along with the calculated trauma scores, were encoded according to a standard Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) coding system, and the trauma information was logged to electronic health records using Fast Health Interoperability Resources (FHIR) servers. The FHIR servers provided interoperable web services to log the trauma event information in real time and to prepare for medical emergencies.

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Reconsidering What is Vital about Vital Signs in Electronic Health Records: Comment on Matthews et al. (2016)

10.31234/osf.io/5q6j8 ◽

2017 ◽

Author(s):

David M Condon ◽

Sara J Weston ◽

Patrick Hill

Keyword(s):

Electronic Health Records ◽

Patient Care ◽

Vital Signs ◽

Clinical Settings ◽

Institute Of Medicine ◽

Psychosocial Variables ◽

Inclusion Criteria ◽

Social Psychological ◽

Health Records ◽

Electronic Health

The inclusion of psychosocial variables into electronic health records provides a unique opportunity for the translation of findings from social, psychological, and behavioral domains into patient care. This commentary is a response to the recommendations of a committee convened by the Institute of Medicine to address this opportunity (Matthews et al., 2016). We concur with the committee that the inclusion of psychosocial variables in electronic health records will broadly benefit researchers, practitioners, and patients and that there is clear need for a recommended panel of psychosocial measures that is ready for implementation in clinical settings. In fact, it seems likely that these recommendations will have lasting consequences. Given this, our response highlights several concerns about the recommendations and criteria. We suggest further clarification of the audience for these recommendations, reconsideration of the overly restrictive inclusion criteria, and more extensive engagement of psychosocial researchers in order to achieve broader consensus.

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