NURS 6051 Week 9 Informatics Literature Review Example

Vital Sign Device Integration and Nursing Documentation: A Review of Five Studies on Efficiency and Data Quality

Student Name

College of Nursing, Walden University

NURS 6051: Transforming Nursing and Healthcare Through Technology

Instructor Name

Month Day, Year

Vital Sign Device Integration and Nursing Documentation: A Review of Five Studies on Efficiency and Data Quality

Introduction and Review Question

Device integration is the direct transfer of data from bedside devices, such as vital sign monitors, into the electronic health record without manual transcription. On many units, nurses still write vital signs on paper or a worksheet and key them into the record later, a practice that invites transcription errors and delays. This review asks: what does published research suggest about the effect of vital sign device integration on nursing documentation efficiency and data quality? The question matters for a composite 32-bed orthopedic unit considering integration of its spot-check vital sign monitors.

How the Five Were Found

CINAHL and MEDLINE through PubMed were searched using combinations of vital signs, automated documentation, device integration, and electronic health records, limited to peer-reviewed research in English. Studies were included if they compared manual and automated vital sign documentation or evaluated an automated system's effect on accuracy, timeliness, completeness, or nurses' workload. Descriptive articles without data and vendor reports were excluded. This is a focused review of five studies, not a systematic review, and it may miss relevant work.

Summary Table

Smith et al. (2009). Design: comparison of error rates before and after implementation, using a prior baseline. Setting: hospital units in the United States. Measures: vital sign documentation errors. Main finding: automated wireless upload from monitors into the record reduced the documentation error rate to under 1 percent, compared with earlier baseline rates of 10 percent for paper and 4.4 percent for manual entry into the electronic record.

Fieler et al. (2013). Design: pretest-posttest. Setting: US community hospital. Measures: error rate and time from measurement to availability in the record, plus clinician questionnaires. Main finding: the paper-then-keyed method had an error rate of 18.75 percent and the wireless method 0 percent; average time until vital signs appeared in the record fell from about 39 minutes to about 5 minutes.

Skyttberg et al. (2017). Design: retrospective analysis of vital sign data from 335,027 emergency visits at five hospitals with paper, mixed, or fully electronic documentation. Setting: Swedish emergency departments. Measures: currency, completeness, and correctness. Main finding: correctness was acceptable across practices, but currency was acceptable only with fully electronic documentation, and completeness was low where documentation was not routinely electronic.

Skyttberg et al. (2018). Design: observational paired comparison of 200 measurements plus workload questionnaires. Setting: Swedish emergency care. Measures: time to documentation, completeness, perceived workload. Main finding: automatic documentation reduced time to documentation from 7.7 to 0.6 minutes, raised completeness from 95 to 98 percent, and lowered staff-reported time pressure and frustration, though frustration shifted toward technical problems.

Cheng et al. (2022). Design: retrospective usage analysis with a cross-sectional survey of 168 nurses and interviews with 20. Setting: hospital in Taiwan. Measures: device use and nurse satisfaction. Main finding: use increased with familiarity and nurses were generally satisfied, but system downtime, battery life, transmission speed, and scanner sensitivity were identified as barriers.

Synthesis: Efficiency

Three studies measured time, and they agree on direction: integration shortens the interval between taking vital signs and their appearance in the record. The magnitude differed, from minutes saved in emergency care (Skyttberg et al., 2018) to a much larger drop where the manual process involved writing on paper and entering later (Fieler et al., 2013). The consistent finding is about currency, whether data are available when a clinician looks, rather than about how much total nursing time is saved. None of the five studies measured whether time saved in documentation was redirected to patient care, which is the efficiency claim most often made for integration. Workload findings add nuance: staff reported less time pressure, but frustration did not fall as much as they expected, because it moved from double documentation to technical failures (Skyttberg et al., 2018), a pattern echoed in nurses' complaints about downtime, batteries, and transmission speed (Cheng et al., 2022). Efficiency gains, in other words, depend on the reliability of the devices and the network.

Synthesis: Data Quality

On accuracy, the studies agree strongly. Both US studies found large reductions in transcription errors with automated transfer (Smith et al., 2009; Fieler et al., 2013). The Swedish multi-site study adds an important qualification: correctness was acceptable even with manual practices, while the clear differences were in completeness and currency (Skyttberg et al., 2017). That suggests the main quality benefit of integration may be having complete and current data, which matters for early warning scores that depend on recent values, as much as eliminating keying errors. There is a gap here too. The studies examine whether values were transferred correctly, not whether the measurement itself was accurate; an integrated system faithfully transfers a reading from a poorly placed cuff. Conflicts between studies are mostly differences in baseline: the worse the manual process, the larger the measured improvement. The evidence base is also dated and small, with two of five studies more than ten years old and few from inpatient orthopedic or surgical units.

Back to the Orthopedic Unit

For the orthopedic unit, the evidence suggests integration would change a nurse's hourly and four-hourly rounding in specific ways. The nurse would scan the patient's wristband, take vital signs with the connected monitor, confirm the values on screen, and accept them into the record at the bedside, instead of writing them on a worksheet and entering them at the station later. Values would appear in the record within minutes, supporting the unit's early warning score for postoperative deterioration, and transcription errors would largely disappear. What would stay the same is the assessment itself: the nurse still decides whether a low blood pressure reflects pain medication, bleeding, or a displaced cuff, still counts respirations where the monitor does not, and still documents pain scores and neurovascular checks manually. The evidence also warns that benefits depend on reliable devices, so the unit should plan for battery management, a downtime procedure, and training before go-live, and it should measure its own time-to-documentation and error rates before and after so that it adds evidence from an orthopedic setting.

References

Cheng, Y.-C., Lee, T.-T., Hwang, Y.-T., Chan, P.-T., & Mills, M. E. (2022). Exploring the outcomes and satisfaction of automated physiological monitoring systems among nurses. CIN: Computers, Informatics, Nursing, 40(3), 178-185. https://doi.org/10.1097/CIN.0000000000000810

Fieler, V. K., Jaglowski, T., & Richards, K. (2013). Eliminating errors in vital signs documentation. CIN: Computers, Informatics, Nursing, 31(9), 422-427. https://doi.org/10.1097/01.NCN.0000432125.61526.27

Skyttberg, N., Chen, R., Blomqvist, H., & Koch, S. (2017). Exploring vital sign data quality in electronic health records with focus on emergency care warning scores. Applied Clinical Informatics, 8(3), 880-892. https://doi.org/10.4338/ACI-2017-05-RA-0075

Skyttberg, N., Chen, R., & Koch, S. (2018). Man vs machine in emergency medicine: A study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires. BMC Emergency Medicine, 18(1), 54. https://doi.org/10.1186/s12873-018-0205-2

Smith, L. B., Banner, L., Lozano, D., Olney, C. M., & Friedman, B. (2009). Connected care: Reducing errors through automated vital signs data upload. CIN: Computers, Informatics, Nursing, 27(5), 318-323. https://doi.org/10.1097/NCN.0b013e3181b21d65