Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses –...

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Improving Accuracy of Vital Improving Accuracy of Vital Signs Capture Using Bedside Signs Capture Using Bedside Computerized Reminders for Computerized Reminders for Nurses – Nurses – Preliminary Results Preliminary Results Philip J. Kroth, Philip J. Kroth, M.D. M.D. ARUP Laboratories ARUP Laboratories April 16, 2003 April 16, 2003

Transcript of Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses –...

Page 1: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

Improving Accuracy of Vital Signs Improving Accuracy of Vital Signs Capture Using Bedside Capture Using Bedside

Computerized Reminders for Computerized Reminders for Nurses – Nurses –

Preliminary ResultsPreliminary Results

Philip J. Kroth, M.D.Philip J. Kroth, M.D.ARUP LaboratoriesARUP Laboratories

April 16, 2003April 16, 2003

Page 2: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

OverviewOverview

RationaleRationale PurposePurpose Bedside Gopher Workstation DemoBedside Gopher Workstation Demo MethodsMethods ResultsResults LimitationsLimitations ConclusionsConclusions

Page 3: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

Rationale – General

Problems associated with the capture of Problems associated with the capture of clinical, bedside measures are significantclinical, bedside measures are significant

Many are technique and skill dependentMany are technique and skill dependent Large data volume:Large data volume:

– 1 million bedside measures per year (96 beds)1 million bedside measures per year (96 beds)– 2700+ measures/day (96 beds)2700+ measures/day (96 beds)– ~30 measures/day/bed~30 measures/day/bed

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Rationale – General

Examples:Examples:– Weights: Notoriously change in large Weights: Notoriously change in large

increments that do not correlate with the increments that do not correlate with the clinical pictureclinical picture

– Blood pressures: Dependent on cuff size, cuff Blood pressures: Dependent on cuff size, cuff placement, and patient positionplacement, and patient position

– TemperaturesTemperatures

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Rationale – Our ProjectRationale – Our Project

Pilot internal study showed 7% of Pilot internal study showed 7% of temperatures stored on medical surgical temperatures stored on medical surgical wards were inaccurately low (wards were inaccurately low (96.4°F)96.4°F)

Causes of inaccuracy:Causes of inaccuracy:– Poor technique/Poor probe placementPoor technique/Poor probe placement– Recent ingestion of hot or cold beveragesRecent ingestion of hot or cold beverages– Recent smoking or bathingRecent smoking or bathing– Poor patient cooperation (i.e. poor mental status etc.)Poor patient cooperation (i.e. poor mental status etc.)

Page 6: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

PurposePurpose

To determine whether computerized, To determine whether computerized, bedside remindersbedside reminders (i.e. real-time bedside (i.e. real-time bedside feedback) can improve the accuracy of feedback) can improve the accuracy of bedside measures (bedside measures (temperature temperature measurementmeasurement))

To determine how the level of training (i.e. To determine how the level of training (i.e. RN, LPN, Aide, etc.) correlates with the RN, LPN, Aide, etc.) correlates with the accuracy of bedside measures accuracy of bedside measures ((temperature measurestemperature measures))

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Methods – Our ProjectMethods – Our Project

Assigned nurses to control or intervention Assigned nurses to control or intervention (reminder) groups by username check digit.(reminder) groups by username check digit.

Automatically included all nurses who worked at Automatically included all nurses who worked at least one shift on any of the four medical-least one shift on any of the four medical-surgical wards in Wishard Hospitalsurgical wards in Wishard Hospital

Preliminary data analyzed from 12:00 AM Preliminary data analyzed from 12:00 AM February 11 to 12:00 PM, March 9, 2003 – Study February 11 to 12:00 PM, March 9, 2003 – Study is ongoingis ongoing

Data from 70 bedside Gopher workstations that Data from 70 bedside Gopher workstations that serve 96 beds on all four Wishard medical-serve 96 beds on all four Wishard medical-surgical wardssurgical wards

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Methods – Intervention Methods – Intervention GroupGroup

A computerized reminder appears when A computerized reminder appears when the user attempts to store a temperature the user attempts to store a temperature 96.4 °F96.4 °F

The user can then either:The user can then either:1.1. Cancel temp and retakeCancel temp and retake

2.2. Cancel temp and do nothing Cancel temp and do nothing

3.3. Over-ride the reminder and store temperature but Over-ride the reminder and store temperature but then must provide a reason for doing sothen must provide a reason for doing so

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Methods – Control GroupMethods – Control Group

No reminder appears when trying to No reminder appears when trying to store a temperature store a temperature 96.4 °F but data 96.4 °F but data is still recorded in the usual manneris still recorded in the usual manner

This is the current standard of care in This is the current standard of care in the hospital today.the hospital today.

Page 10: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

Bedside Gopher WorkstationBedside Gopher Workstation

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Direct Data Capture

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Keyboard Entry

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Verify & Store Data

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Verify & Store Data

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Retake Temp

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Verify & Store Data

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Store & Override

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Store & Reason

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Return to Order Entry System

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Study ResultsStudy Results

191 unique nurses 191 unique nurses 7985 temperature measures7985 temperature measures 709 unique patients709 unique patients 42 temp’s/nurse42 temp’s/nurse 11 temp’s/patient11 temp’s/patient

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Results – Subjects By Nursing Results – Subjects By Nursing TypeType

Control Reminder

RN: 34 (58%) 25 (42%)

LPN: 16 (47%) 18 (53%)

Aides: 26 (45%) 32 (55%)

Other: 20 (50%) 20 (50%)

Total: 96 (50%) 95 (50%)

Page 43: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

Results -- OverallResults -- Overall

320 Low temps recorded320 Low temps recorded 0.45 Low temps/patient (average)0.45 Low temps/patient (average) 1.7 Low temps/nurse (average)1.7 Low temps/nurse (average) 85% of low temp’s repeated because of a 85% of low temp’s repeated because of a

reminder are in the normal range.reminder are in the normal range.

Page 44: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

ResultsResults

5.7% Low temps recorded in control group5.7% Low temps recorded in control group 2.9% Low temps recorded in reminder 2.9% Low temps recorded in reminder

groupgroup 2.8% overall 2.8% overall absoluteabsolute risk reduction risk reduction 51% overall 51% overall relativerelative risk reduction in risk reduction in

reminder vs. control group.reminder vs. control group.

Page 45: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

Results – By Temperature Results – By Temperature

All Temps (7985)

Temps in Control Group (3641)

Temps in Reminder Group (4344)

Other Temps (4220)

Low Temp’s (124) 2.9%

Other Temps (3445)

Low Temp’s (196) 5.7%

51% RelativeRisk Reduction

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Results – by Nursing TypeResults – by Nursing Type

RecordedTemperatures

Recorded Low Temperatures

Control Reminder

RN: 1186 (15%) 27 (2.3%) 10 (0.8%)

LPN: 589 (7%) 12 (2.0%) 8 (1.3%)

Aides: 5996 (75%) 150 (2.5%) 101 (1.7%)

Other: 214 (3%) 7 (3.3%) 5 (2.3%)

Page 47: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

Results – By Reminder Results – By Reminder

Number of Low Temp Conditions (466)

Not Prompted (203)Controls

Prompted (236)Intervention

Stored (120)

Canceled (143)

Not Repeated (20)

Repeated (123)

Temp > 96.4(104) 85%

Temp 96.4 (19) 15%

Page 48: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

ConclusionsConclusions

Inaccurately recorded bedside Inaccurately recorded bedside temperatures are very common (5.7%!)temperatures are very common (5.7%!)

Reminders for nurses at the beside can Reminders for nurses at the beside can improve the accuracy of temperature improve the accuracy of temperature measurementmeasurement

The effect of the reminder may be The effect of the reminder may be associated with the level of trainingassociated with the level of training

Page 49: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

LimitationsLimitations

Small n – but data collection is ongoing.Small n – but data collection is ongoing. No statistical power yet but should have No statistical power yet but should have

after three month’s of dataafter three month’s of data

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Future WorkFuture Work

Improve accuracy of other patient Improve accuracy of other patient measures, i.e. weight, blood pressuremeasures, i.e. weight, blood pressure

Capture additional bedside parameters, Capture additional bedside parameters, i.e. pain scale, peak flows, and othersi.e. pain scale, peak flows, and others

Page 51: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

Thanks toThanks to

Clement J. McDonald, M.D. Clement J. McDonald, M.D. Anne Belsito, M.S.Anne Belsito, M.S. Paul Dexter, M.D.Paul Dexter, M.D. J. Marc Overhage, M.D., PhD.J. Marc Overhage, M.D., PhD. Siu Hui, PhD.Siu Hui, PhD.

Page 52: Improving Accuracy of Vital Signs Capture Using Bedside Computerized Reminders for Nurses – Preliminary Results Philip J. Kroth, M.D. ARUP Laboratories.

Improving Accuracy of Vital Signs Improving Accuracy of Vital Signs Capture Using Bedside Capture Using Bedside

Computerized Reminders for Computerized Reminders for Nurses – Nurses –

Preliminary ResultsPreliminary Results

Philip J. Kroth, M.D.Philip J. Kroth, M.D.ARUP LaboratoriesARUP Laboratories

April 16, 2003April 16, 2003