Hospital Noise

Analysis TeamEnd-of-Year Project Summary

December 5th, 2012Matthew Ditullio (IOE)

Victoria Ranger IOE)Keegan Cisowski (ChE)Patrick Connolly (PAT)

Forecast Background Results and Analysis

› Data Acquisition› Implementation

Conclusions Recommendations Future of Project

Background Formed in January through MDP Diagnose problem with noise levels 7A/7B Goal: Reduce noise levels? Re-analyzed problem Link between rest and noise levels New Goal: Change perception of noise

Background Worked with ISR to create survey Interviewed staff and patients Noise Inventory Collected and analyzed data Further research Determined short-/long-term changes Implemented short-term changes Interviewed patients Collected and analyzed results

Patient/Staff Interview ResultsStaff Sample Size: 35Patient Sample Size: 33

Staff Interview Conclusions

Have worked in hospital setting for < 10 years

50%

Have been working at this particular unit between 2 and 5 years

60%

Believe the loudest time of day is 6am-noon

57%

Believe alarms/medical equipment impede a patients ability to rest

69%

Believe maintenance/doors impede rest

67%

Patient Interview Conclusions

Length of stay of patients 33% have been here over 1 week

Average hospital stay rating 4.59/5 (5 is the best)

Overall stay rating a 5/5 64%

Believe they have perfect hearing

67%

Average room rating 2.4/5 (5 is loud)

Believe that the loudest time of day is 6am-noon

52%

Actions Taken by Staff

Close door/Cur-tain39%

Explain about the beds/un-plug/switch

bed22%

Gave complaint to supervisors/head

nurse12%

Ask people/staff to quiet down

12%

No complaints7%

Offer ear plugs2%

Quiet the monitor2%

Apologize2%

Staff Suggestions (7A/7B)

Close/Modify doors33%

Less talking of staff/remind one another

21%

New beds14%

Quieter machines/moni-

tors10%

Modify walls/ceilings/floor to break

and absorb the sounds

7%

Alter Alarms7%

No ideas5%

Add white noise2%

Patient Suggestions (7A/7B)

No complaints or ideas/fine how it

is50%

Earplugs13%

Cart maintenance4%

Door mainte-nance/oil hinges

4%

All Private rooms4%

Stop IVs from beep-ing so easily or when nothing is

wrong4%

Discharge the pa-tient4%

White noise/sound machines

4%

Get quieter toilets4%

Keep the door shut4%

Have people just be more aware

4%

Sound Disruptiveness Pareto Chart

Phon

eBed

Doors

Trash

Can/Main

tence

IV Alarm

Beds R

olling

Page

rCart

Floor

Cleane

rPri

nter

02468

10121416

0%

20%

40%

60%

80%

100%

Post-Survey Implementations

Possible Implementations

Phones› Operational: Close doors, Designated Cell

Phone Areas (by elevators), Turn Down Volume at Night/During Quiet Hours

› Design: Play White Noise through Headphones, Sound Absorbent Ceiling/Wall Materials

Beds› Operational: Use different beds› Design: Play White Noise through Headphones

Possible Implementations

Doors› Design: Change Latches, Play White Noise

through Headphones, Sound Absorbent Ceiling/Wall Materials, Put Windows on Doors

Maintenance› Operational: Maintain Equipment

(Wheels!), Dim Lights During Night/Quiet Hours, Close Doors

› Design: Put “Shhhhh!” Signs on All Doors

Possible Implementations

IV Alarms› Design: Modify Notification Method, Play

White Noise through Headphones

Uncategorized Noises› Operational: Special Admittance

Hours/Location, Play Nature Sounds/Soothing Music

› Design: Install Fans to Mask Noise, Carpet Tube Delivery Systems

Actual Implementations 7A

› “Shhhhh” signs on doors to patient rooms, supply closets, and bathrooms

7B› “Quiet Hour” from 1 – 2 pm

Doors closed, lights dimmed, staff focus on being quiet

› White noise machine trials in patient rooms Post-implementation surveys to

administer for 7A/7B

Post-Implementation Survey ResultsPatient Sample Size: 37

7A Post-Implementation Results

Mention of morning as loudest time of day increased› Before: 31.25% › After: 60%

Mention of patients/families as noise contributors decreased by 47%› Before: 25%› After: 13.3%

Mention of hospital staff as noise contributors decreased by 29%› Before: 37.5%› After: 26.7%

7A Post-Implementation Results

Overall stay rating shows no major change › 0 – 5; 5 is best› Before: 4.41› After: 4.07

Noise in room shows no major change› 0 – 5; 5 is loudest› Before: 1.72› After: 1.8

Percentage of patients believing noisiness can be changed decreased by 24%› Before: 43.75%› After: 33.33%

7A Post-Implementation Results

Mention of alarms/medical equipment as noise contributors increased by 49%› Before: 31.25%› After: 46.7%

Mention of everyday noises as noise contributors increased by 433%› Before: 6.25%› After: 33.33%

7A Noise Survey Results

7A Noise Survey Results Patients’ overall perception of alarms,

beeps, and IVs rolling through hall were more negative (non-human-controlled)

Patients’ overall perception of beds rolling through hall, cabinets and doors closing, pagers, phones, trash cans in hall, and floor cleaner (human-controlled)

Conclusions from 7A Morning (6 am to 12 pm) is consistently

perceived as loudest time of day “Shhhhh” signs seem to have made visiting

families and hospital staff more aware of resting environment› Decrease in belief that noisiness can be improved› Decrease in contributions from patients/families and

hospital staff to noise› Decrease in perception of human-controlled noises

Increase in contributions from alarms/medical equipment and everyday noises to noise show possible need for quiet hour and/or white noise

7B Post-Implementation Results

Mention of morning as loudest time of day increased› Before: 43% › After: 62.5%

Mention of patients/families as noise contributors increased by 25%› Before: 10%› After: 12.5%

Mention of hospital staff as noise contributors increased by 51%› Before: 29%› After: 43.75%

7B Post-Implementation Results

Overall stay rating shows no major change › 0 – 5; 5 is best› Before: 4.45› After: 4.1

Noise in room shows minimal change› 0 – 5; 5 is loudest› Before: 1.9› After: 2.47

Percentage of patients believing noisiness can be changed decreased by 22%› Before: 48%› After: 37.5%

7B Post-Implementation Results

38% noticed quiet hours, and 83% of those believed they had good impact

Mention of alarms/medical equipment as noise contributors increased by 150%› Before: 10%› After: 25%

Mention of everyday noises as noise contributors decreased by 61%› Before: 48%› After: 18.75%

7B White Noise Trials Placed white noise machine in middle

of patient room, unbeknownst to patient(s)

Interviewed patient and asked if white noise machine was noticed

White noise machine removed from room

Next day, if still present, comparison of sleep from 1st to 2nd night

7B White Noise Trials Sample size very small Individual cases Case 1

› 1st night: Noticed machine, and said “I really appreciate it…it drowns and mellows out everything…5 on a scale of 0 to 5 of being good”

› 2nd night: Awake most of the night; noticed hallway and people noise through walls

7B White Noise Trials Case 2

› 1st night: Noticed machine; said machine “works” and he “loved the white noise machine” because it “helped a lot” and was very upset about it being moved

› 2nd night: Sleep was worse than 1st night; said they should “put them in every room”

Other cases either were not interviewed both nights or did not notice the machine in the room

Conclusions from 7B Morning (6 am to 12 pm) is consistently perceived

as loudest time of day Quiet hours seem to have reduced impact of

everyday noises on patients’ rest White noise machines seem to aid patients’ sleep Increase in contributions from patients/families

and hospital staff to noise show possible need for “Shhhhh” signs

Increase in contribution from alarms/medical equipment to noise show possible need for more white noise

Recommendations Continue quiet hour in 7A/7B Increase amount of white noise

machines available for patients to use Put up more “Shhhhh” signs to spread

awareness in 7A/7B

Project in 2013 4 new team members We will be moving to mentoring role Team will focus on 2 main projects

› Re-design of IV alarms by patient beds› Implementation of pink noise in patient

rooms

Thank you!Any questions?

Matthew Ditullio – mattdit@umich.eduVictoria Ranger – vranger@umich.edu

Keegan Cisowski – kcisowsk@umich.eduPatrick Connolly – patconno@umich.edu