Humanities and Medicine IMPACT OF VISUAL ART ON PATIENT BEHAVIOR IN THE EMERGENCY DEPARTMENT WAITING ROOM Upali Nanda, PHD, ASSOC AIA, EDAC,* Cheryl Chanaud, PHD,† Michael Nelson, FACHE,‡ Xi Zhu, BS,‡ Robyn Bajema, BS,* and Ben H. Jansen, PHD‡jj *Research Department, American Art Resources, Houston, Texas, †Clinical Innovation and Research Institute, Memorial Hermann Hospital, Houston, Texas, ‡Department of Electrical and Computer Engineering, and jjDepartment of Biomedical Engineering, University of Houston, Houston, Texas Reprint Address: Upali Nanda, PHD, ASSOC AIA, EDAC, Research Department, American Art Resources, 3260 Sul Ross, Houston, TX 77098 , Abstract—Background: Wait times have been reported to be one of the most important concerns for people visiting emer- gency departments (EDs). Affective states significantly impact perception of wait time. There is substantial evidence that art depicting nature reduces stress levels and anxiety, thus poten- tially impacting the waiting experience. Study Objectives: To analyze the effect of visual art depicting nature (still and video) on patients’ and visitors’ behavior in the ED. Methods: A pre–post research design was implemented using systematic behavioral observation of patients and visitors in the ED wait- ing rooms of two hospitals over a period of 4 months. Thirty hours of data were collected before and after new still and video art was installed at each site. Results: Significant reduc- tion in restlessness, noise level, and people staring at other peo- ple in the room was found at both sites. A significant decrease in the number of queries made at the front desk and a signifi- cant increase in social interaction were found at one of the sites. Conclusions: Visual art has positive effects on the ED waiting experience. Ó 2012 Elsevier Inc. , Keywords—positive distraction; waiting experience; nature; art; emergency department INTRODUCTION Wait times have been reported to be one of the most im- portant concerns of emergency department (ED) patients (1–3). According to Pruyn and Smidts, the adverse effects of waiting can be soothed more effectively by improving the attractiveness of the waiting environment than by shortening the objective waiting time (4). Impact of the designed environment on mood is supported by research by Becker and Douglass showing that physical attractive- ness of a waiting room impacted anxiety levels and per- ception of quality of care to a greater degree than actual waiting time (5). According to Zakay, although the design element of the waiting environment can induce a positive mood, influencing the appraisal of the wait (without nec- essarily influencing perceived duration of the wait), ex- plicit or foreground distractors in the waiting environment can affect the cognitive timer (internal clock) by means of diverting attention away from the pas- sage of time (6). In the case of health care environments in general, and ED waiting in particular, affective response plays an im- portant role. In a small-scale observation of the ED wait- ing room, distrust between the patients and staff of the hospital, patients’ consistent focus on their status, and an uncertainty about the waiting time have been identified as contributors to patient anxiety, fear, confusion, and an- noyance (7). Gordon et al. identified anxiety, stress, fear, and pain as emotions experienced by patients in the ED (8). Positive distractions in the environment potentially can change the patient focus from the patient’s own status and improve the affective state. RECEIVED: 7 January 2011; FINAL SUBMISSION RECEIVED: 21 March 2011; ACCEPTED: 5 June 2011 172 The Journal of Emergency Medicine, Vol. 43, No. 1, pp. 172–181, 2012 Copyright Ó 2012 Elsevier Inc. Printed in the USA. All rights reserved 0736-4679/$ - see front matter doi:10.1016/j.jemermed.2011.06.138
Transcript
The Journal of Emergency Medicine, Vol. 43, No. 1, pp. 172–181, 2012Copyright � 2012 Elsevier Inc.
Printed in the USA. All rights reserved0736-4679/$ - see front matter
doi:10.1016/j.jemermed.2011.06.138
RECEIVED: 7 JanACCEPTED: 5 Jun
Humanitiesand Medicine
IMPACT OF VISUAL ART ON PATIENT BEHAVIOR IN THE EMERGENCYDEPARTMENT WAITING ROOM
Upali Nanda, PHD, ASSOC AIA, EDAC,* Cheryl Chanaud, PHD,† Michael Nelson, FACHE,‡ Xi Zhu, BS,‡Robyn Bajema, BS,* and Ben H. Jansen, PHD‡jj
*Research Department, American Art Resources, Houston, Texas, †Clinical Innovation and Research Institute, Memorial Hermann Hospital,Houston, Texas, ‡Department of Electrical and Computer Engineering, and jjDepartment of Biomedical Engineering, University of Houston,
Houston, Texas
Reprint Address: Upali Nanda, PHD, ASSOC AIA, EDAC, Research Department, American Art Resources, 3260 Sul Ross, Houston, TX 77098
, Abstract—Background:Wait times have been reported tobeoneof themost important concerns forpeople visiting emer-gency departments (EDs). Affective states significantly impactperception of wait time. There is substantial evidence that artdepicting nature reduces stress levels and anxiety, thus poten-tially impacting the waiting experience. Study Objectives: Toanalyze the effect of visual art depicting nature (still andvideo) on patients’ and visitors’ behavior in the ED. Methods:A pre–post research design was implemented using systematicbehavioral observation of patients and visitors in the EDwait-ing rooms of two hospitals over a period of 4 months. Thirtyhours of data were collected before and after new still andvideo art was installed at each site. Results: Significant reduc-tion in restlessness, noise level, and people staring at other peo-ple in the room was found at both sites. A significant decreasein the number of queries made at the front desk and a signifi-cant increase in social interaction were found at one of thesites. Conclusions: Visual art has positive effects on the EDwaiting experience. � 2012 Elsevier Inc.
, Keywords—positive distraction; waiting experience;nature; art; emergency department
INTRODUCTION
Wait times have been reported to be one of the most im-portant concerns of emergency department (ED) patients(1–3). According to Pruyn and Smidts, the adverse effects
uary 2011; FINAL SUBMISSION RECEIVED: 21 Mare 2011
172
of waiting can be soothed more effectively by improvingthe attractiveness of the waiting environment than byshortening the objective waiting time (4). Impact of thedesigned environment on mood is supported by researchby Becker and Douglass showing that physical attractive-ness of a waiting room impacted anxiety levels and per-ception of quality of care to a greater degree than actualwaiting time (5). According to Zakay, although the designelement of the waiting environment can induce a positivemood, influencing the appraisal of the wait (without nec-essarily influencing perceived duration of the wait), ex-plicit or foreground distractors in the waitingenvironment can affect the cognitive timer (internalclock) by means of diverting attention away from the pas-sage of time (6).
In the case of health care environments in general, andED waiting in particular, affective response plays an im-portant role. In a small-scale observation of the ED wait-ing room, distrust between the patients and staff of thehospital, patients’ consistent focus on their status, andan uncertainty about thewaiting time have been identifiedas contributors to patient anxiety, fear, confusion, and an-noyance (7). Gordon et al. identified anxiety, stress, fear,and pain as emotions experienced by patients in the ED(8). Positive distractions in the environment potentiallycan change the patient focus from the patient’s own statusand improve the affective state.
A positive distraction is an environmental feature thatelicits positive feelings and holds attention without taxingor stressing the individual, thereby blocking worrisomethoughts (9). In the medical community, art interventionsare often used as positive distractions for patients. For ex-ample, adult patients in a procedure room reported betterpain control when exposed to a nature scene with naturesounds coming from speakers in the ceiling (10). Use ofmurals (as distraction) resulted in a significant decreasein reported pain intensity, pain quality, and anxiety ofburn patients (11). Breast cancer patients reportedreduced anxiety during chemotherapy when exposed tovirtual reality intervention displaying underwater scenes(12). In fact, there is a growing body of evidence arguingthat visual art depicting nature scenes can improve thepatient experience through reduced stress, anxiety, painperception, and improved perception of quality of care(13–20). Research shows that even short-term visualcontact with nature can be restorative. Physiologicaldata collected via skin conductance, muscle tension,and pulse transit time from subjects who watched photo-graphic simulations of natural settings showed faster re-covery than subjects who viewed simulated urbansettings (9). Based on this evidence, guidelines for healthcare art are in place that recommend art with views or rep-resentations of nature containing calm or slowly movingwater, verdant foliage, flowers, foreground spatial open-ness, and park-like or savannah-like properties (scatteredtrees, grassy undergrowth) (20).
Whereas the studies discussed above make a strongcase that visual art depicting nature scenes can reducestress, anxiety, and pain perception, few studies havemade the connection between art, patient experience,and patient behavior. In a study with pediatric patients,display of different nature-based video conditions viaa plasma television (TV) screen improved attention andoverall calm behavior in children waiting in dental andcardiac clinics (19). The objective of this study was to in-vestigate the impact of a positive distraction (visual art)on adult behavior in two high-stress ED waiting rooms.
METHODS
This study was designed as a pre-intervention/post-intervention study, with an extensive art installation serv-ing as the intervention. Two ED waiting rooms wereidentified as the sites for this project: Ben Taub GeneralHospital (hereafter referred to as ED1) with between100,000 and 105,000 ED visits per year, and MemorialHermann Hospital (hereafter referred to as ED2) with be-tween 40,000 and 45,000 ED visits per year. Both hospi-tals are located in the Texas Medical Center and serve as
Houston’s only two Level I Trauma Centers. The layoutsfor the two hospitals are shown in Figure 1.
A systematic behavioral observation tool was devel-oped to study the behaviors of patients in the waitingroom. The independent variable, art intervention, was de-signed based on previous literature in the field ofevidence-based art, and is discussed in the next section.The dependent variable, subject behavior, was measuredquantitatively by systematic observation. Two types ofbehaviors were identified: continuous behaviors, suchas reading, dozing, or watching TV, which occur overa period of time and cannot be counted; and discrete be-haviors, such as getting out of seat, changing seats,stretching, and pacing, which are specific events thatcan be counted. Continuous behaviors were annotated us-ing symbols on a to-scale layout map of the emergencywaiting area (including furniture) for a period of 5 min,every 20 min, to obtain the ‘‘behavior map.’’ The follow-ing behaviors were annotated: cell phone usage (listen-ing/talking/texting); viewing TV; looking at the artintervention (plasma screen or artwork); reading; talkingto other patients or family; looking at other people (peo-ple-watching); dozing (in chair); lying down (on chair oron the floor); looking out of the window; fidgeting (rest-less movements such as shaking knees, twiddling fingers,tapping feet, etc.); eating or drinking; working on a lap-top; and other activities.
Discrete behaviors were marked by tallymarks, in a ta-ble format, each time the following behaviors occurred inthe room within a 5-min period, to obtain the FrequencyCount:
1. Enter Waiting Room2. Front Desk Queries (person asking question at the
front desk)3. People Pacing (person walking back and forth)4. Aggressive Behavior (shouting, pushing, shoving,
etc.)5. Out of Seat (getting out of seat)6. Changing Seat (changing to new seat)7. Stretching (extending arms or legs, neck, back, etc.)8. Exit Waiting Room
The observation instrument consisted of nine fre-quency counts, four behavior maps, and four noise mea-surements. Location of where the observers sat is shownin Figure 1. At the bottom of each behavior map, the totalnumber of people in the ED was noted.
A data collection plan was developed based on a uni-form sampling of peak and non-peak times, differenttimes of the day, and different days of the week. Thirtyhours of data were collected pre and post art interventionin each site.
Definitions of each behavior were discussed betweenthe Principal Investigator and the observers, and a pilot
Figure 1. Emergency department layouts showing locationof observers (O) and noise meter reading (N).
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study was conducted to ensure inter-rater reliability(found to be 95%). The observation instruments were re-fined to cater to each site’s unique layout and challenges.In ED1, there were occasional changes in layout; chairscould be added when the volumewas high and the config-uration could be modified slightly as needed. Therefore,a fixed position for the observers was not possible. Beforeeach data collection period, observers identified, from theseats available, the best seat from which to observe themaximum number of people, and annotated the positionof the seat on the map. Sightlines and visibility of patientsdid not vary due to careful seat selection. Due to the largewaiting area, two observers were needed for every obser-vation in ED1 (see Figure 1). The waiting room was di-vided in two, and two versions of the data collectioninstrument were created based on activities that couldbe observed in each position. For example, the observerin the first section would count things like entrance/exitof the waiting area, and the person in the second sectionwould count the entrance/exit of examination rooms.Other behaviors, like getting out of seat or changing seats,were counted by both observers for their respective sec-tions. Data from the two sheets were combined duringdata entry.
In ED2, the seating was fixed, and a seat for the ob-server was available (Figure 1). Due to the layout of thewaiting room (four bays on one side and four on the otherside of the entrance) it was difficult for a single observerto observe both sides of the entrance. The pilot studyshowed that the bays on the right side of the entrancewere seldom used, and were used more often by staffthan patients. For this reason, the art was installed inthe left part of the waiting area only (shown inFigure 1). Discrete behaviors were observed for this sec-tion only. Continuous behaviors were observed for the en-tire waiting room, because the observer could get up andwalk down the entire length to annotate behaviors.
Every person in the waiting room area visible to theobserver within the selected area (Figure 1) was observedregardless of whether he or shewas a patient or an accom-panying family member or friend (visitor). Staff memberswith official badges were excluded from the study. Chil-dren were noted on the behavior map, but their behaviorwas not observed. Observers made a determination onwho was a child based on physical appearance; therefore,it is possible that children who looked older were in-cluded and adults who looked younger were excludedfrom the study. Observers wore official badges to ensurelack of interference.
Institutional Review Board (IRB) approval was ob-tained before conducting the study. The informed consentrequirement was waived by the IRB because no patientidentifiers were collected.
Observers noted their own qualitative comments onthe sheets, including comments that they overheardfrom patients and staff, operational differences in theED, traffic patterns, unusual behaviors, and changes inthe environment or layout. Noise levels were recordedfour times during the course of the observation usinga UEI DSM101 sound level meter for environmentalsound measurement. Noise was always measured at thesame location at both sites—in the center of the waitingroom (Figure 1). Observers made notes if there were spe-cial circumstances during a reading such as someone yell-ing or an intercom announcement.
Art Intervention
Video.A continuous video loop of approximately 20 minwas developed from a set of 126 images—33 florals, 25landscapes, and 38 waterscapes. Four contributing artists,whowere identified for their nature photography, donatedimages. In ED1, due to the large size of the waiting room,three plasma television screens were placed (integratedwith wood paneling) for the video. The videos wereplayed at different time points so the three screens haddistinct images at different times. In ED2, one plasmascreen was adequate.
Figure 2. Art installations in Emergency Department (ED)1 and ED2; images for canvas art and window film (ED1) by MonteNagler; video art provided by Monte Nagler, Bill Robertson, Ann Parks, and David Burt.
Impact of Visual Art in the ED 175
Still art.Still art usedwas installed at the sites, based on theproportions of the space. Six images that followed theguidelines for appropriate health care art were printed oncanvas and wrapped around the frame to give a ‘‘win-dow-like’’ effect (20). The impact of nature art andwindowviews has been established in previous literature (20). InED1, two large canvases (132’’ � 40’’), one mediumcanvas (54’’ � 36’’), and one small canvas (30’’ � 40’’)were installed. In ED2, two medium canvases (60’’ �40’’) and three small canvases (30’’� 40’’) were installed.
Window film.Window films were also used on windows atboth sites. At ED1, a large garden scene was used andapplied to three window panels that provided a win-dow-within-a window effect. In ED2, a continuous cloudpattern was used as a border near the top of the windowsto reduce glare issues on screens. Figure 2 shows the artinstallations in the two sites.
Figure 3. Summary statistics (histograms) comparing thepre-intervention vs. post-intervention behavior ratio, plottedas percentages to see the trends in the behaviors.
Analysis
Data from the behavior map and frequency count wereentered into an Excel spreadsheet (Microsoft Corpora-tion, Redmond,WA). Behaviors pre and post art interven-tion were totaled for each hour. Each behavior was thenrepresented as a ratio of the total number of people. Sum-mary statistics (histograms) comparing the pre–post be-havior ratio were plotted as percentages to see thetrends in the behaviors (Figure 3). To analyze the differ-ence in behaviors pre and post intervention while ac-counting for other related factors, observationalvariables were grouped into the following categories:
1. Distraction Activity: These included the activitiesthat people were engaged in using a particularenvironmental or non-environmental instrument,including talking on cell phone, viewing TV, read-ing, using laptops, and looking out of the window.
2. Non-Distraction Activity: These included activitieswhere an environmental or instrumental distractionwas not being used, such as eating or drinking,looking at other people, talking to other people,dozing, and lying down.
3. Restless/Anxious Behavior: These were behaviorsthat were hypothesized to be indicative of restless-ness and anxiety. These included number of frontdesk queries, getting out of seat, changing seat,pacing, fidgeting, stretching, and aggressive behav-ior (shouting, cursing, shoving, etc.).
Table 1. Top 5 Behaviors Pre and Post Intervention
Pre Intervention ED1 Post Intervention ED1 Pre Intervention ED2 Post Intervention ED2
Getting out of seat Getting out of seat Getting out of seat Getting out of seatPeople watching Talking Talking TalkingTalking People watching People watching Using cell phonesDozing Dozing Viewing TV DozingViewing TV Viewing TV Using Cell phones People watching
ED = emergency department.
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Two-way analyses of variance (ANOVAs) were con-ducted for each of these three behavioral groups. TheBonferroni post hoc test was conducted to test for interac-tion effect between behavior and test condition (pre/post).A significant interaction effect was found between therow and column for non-distraction activity (for onesite) and restless behavior (for both sites). Because the in-teraction between observations (rows) and pre–post (col-umn) was significant, t-tests for each sub-group wereperformed, in addition to the Bonferroni post hoc test dur-ing the ANOVA. Due to the difference in variances,Welch’s correction was used on the t-tests (correctionfor unequal variances is not possible in the Bonferronipost hoc test).
RESULTS
Analysis of the summary statistics revealed that the mostcommon behaviors at both sites, before the intervention,were getting out of seats, talking, watching TV, watchingother people, talking on cell phones, and dozing(Figure 3). After the intervention these behaviors werestill the most common, but there was a change in howthey were ranked. Table 1 shows the top five behaviors
Table 2. ANOVA Results for Distraction/Non-distraction Activities
Analysis ED1
Two-way ANOVA Distraction actSource of variation Df F
ED = emergency department; ANOVA = analysis of variance.
before and after intervention for the two sites. Table 2shows the ANOVA and t-test statistics for distractionand non-distraction activities. Table 3 shows the ANOVAand t-test statistics for restless/anxious behavior. Resultsfrom the ANOVA and t-tests are summarized below.
Distraction Activity
ED1: No significant changes in routine distraction activ-ities (cell phone, TV viewing, reading, laptop usage, andlooking out the window) were observed between pre andpost intervention.
ED2: Looking out the window significantly increased,and watching TV significantly decreased post interven-tion (however, it should be noted that the total numberof TVs showing regular TV programming was lowerpost intervention).
Non-Distraction Activity
ED1: There was a significant reduction in people-watching behavior post intervention. Therewas also a sig-nificant increase in talking (increased socializing). No ad-ditional significant differences in other behaviors were
Bonferroni post-tests (ANOVA) Pre Post t p value Welch’s corrected T-test dfFidgeting 0.0171 0.001228 0.2536 ns 2.820 29 0.0086Front desk queries 0.1023 0.07846 0.3766 ns 0.8140 46 nsPacing 0.2621 0.07514 2.954 < 0.05 1.348 29 nsAggressive behavior 0.0078 0.003009 0.07582 ns 0.8229 41 nsGetting out of seat 0.7498 0.4782 4.292 <0.001 3.142 55 0.0027Changing seat 0.0566 0.02739 0.4612 ns 1.377 51 nsStretching 0.075 0.01409 0.9626 ns 2.282 30 0.0297
ED = emergency department; ANOVA = analysis of variance.
Impact of Visual Art in the ED 177
found in the ANOVA. A significant interaction was foundbetween row and column in the ANOVA, so two-tailed t-test withWelch’s correction was conducted to analyze thedifference in pre and post behavior. ANOVA results forpeople watching and talking were replicated in the t-test.
ED2: There was a significant reduction in peoplewatching behavior post intervention. Significant differ-ences in other behaviors were not found.
Restless/Anxious Behavior
ED1: There was a significant reduction in front deskqueries and out-of-seat behavior post intervention. A sig-nificant interaction effect between rows (behaviors) andcolumns (pre and post) was found in the Bonferronipost hoc test. Therefore, in addition to the Bonferronipost hoc test, t-tests for every sub-group were conducted.Due to the difference in variances, Welch’s correctionwas used on the t-tests. t-Test revealed a significant reduc-tion in pacing and stretching post art intervention, as wellas in the number of front desk queries and out-of-seat be-havior. It is fair to conclude that there was a significant re-duction in restless behavior post art intervention.
ED2: There was a significant reduction in pacing andout-of-seat behavior post intervention. Similar to ED1,a significant interaction effect was found between rows(behaviors) and columns (pre and post) in the two-way
ANOVA. Therefore, in addition to the Bonferroni posthoc tests, t-tests for every sub-group were performed.Due to the difference in variances, Welch’s correctionwas used on the t-tests. The results showed significant re-ductions in fidgeting and stretching.
Noise
Loudness is defined as ‘‘that attribute of auditory sensa-tion in terms of which sounds can be ordered on a scaleextending from quiet to loud’’ (21). Loudness, to a greatdegree, is subjective.
Decibel (dB) is the unit of measure for sound pressurelevel. It is a logarithmic scale developed to express a widerange of quantities on a simple scale (22). Sound pressurelevels in the ED were measured every 20 min, averagedacross each observation (four readings), and comparedpre and post intervention using Mann-Whitney test(non-parametric test that does not assume a Gaussian dis-tribution of data). Sound pressure levels were found to besignificantly lower post intervention in both ED1 andED2. Figure 4 shows the difference in sound pressurelevels for the two sites. Although the total number ofreadings was much lower post intervention compared topre intervention in ED2 (due to unavailability of the noisemeter), homogeneity between peak and non-peak timeswas maintained. Overall, the sound pressure levels were
Figure 4. Differences in sound pressure levels at both sites (pre and post intervention).
178 U. Nanda et al.
higher in ED1, which can be explained by its larger vol-ume of patients. An average reduction of 6 dB was foundfor both sites. A decrease of 6 dB yields one-half of thesound amplitude as the original, and can be clearly no-ticeable to the normal human ear as a significant decreasein apparent loudness (23).
Table 4. Summary of Results Pre and Post Art InterventionAcross ED Sites
Behavior ED1 ED2
Out-of-seatbehavior
Significantdecrease*†
Significantdecrease*†
Pacing Significantdecrease†
Significantdecrease*
People watching Significantdecrease*†
Significantdecrease*†
Talking Significantincrease*
No difference
Front desk queries Significantdecrease*†
No difference
Fidgeting No difference Significantdecrease†
Noise Significantdecrease†
Significantdecrease†
ED = emergency department.* Bonferroni’s post hoc test.† Two-tailed t-test with Welch’s correction.
DISCUSSION
A pre–post research design was used to study the impactof an art intervention on the behaviors of people in twobusy ED waiting rooms. The independent variable, art in-tervention, was based on established guidelines for healthcare art and consisted of nature images in two modalities:a rotating video display and still art printed on canvas andwindow film (20). The dependent variable, subject behav-ior, was measured by using a systematic observation toolfor a period of 65 min. A total of 60 observations (30 be-fore the intervention and 30 after the intervention) weremade per site. The significant pre- and post-interventionchanges at the two ED sites are shown in Table 4.
A significant decrease in restless behavior was foundat both sites post intervention. In ED1 there was a de-crease in out-of-seat behavior, pacing, front desk queries,and stretching. In ED2 there was a decrease in out-of-seatbehavior, pacing, fidgeting, and stretching. We can arguethat the decrease in restlessness came from the presenceof a positive distraction, giving patients somethingpleasant to look at while they were in the waiting room.We can also argue that the content of the distraction,serene nature images that followed specific guidelinesfor selection, were calming in nature. This is furtherreinforced by the reduction in noise levels found inboth the EDs, especially ED1, where there was no change
in environmental noise sources (such as TVs). Thisimplies that the people were talking in softer voices,which is an indication of calm behavior. This has a strongimplication for improving the emotional and affectiveexperience of patients in the waiting area. The reductionof front desk queries found in ED1 has a significantoperational implication: it could result in a decrease instaff time, and staff stress. Implications of patient experi-ence on staff stress warrant further investigation.
A significant decrease in ‘‘people-watching’’ (peoplestaring at other people) was also found post interventionin both sites. This has a strong implication for privacy.Waiting rooms are often set up as open plans, and lookingat other people is one of the most common activities (seeTable 1 and Figure 3). Being looked at can be stressful for
Impact of Visual Art in the ED 179
someone who is feeling unwell or has a specific defor-mity. Any reduction in this behavior can be hypothesizedto have a positive impact on patient stress.
In ED1 there was a significant increase in talking. Thisimplies an increased socializing, regardless of the contentof discussion. Unfortunately, the study design did not al-low observers to note if the discussions were betweenpeople who knew each other or strangers, but in eithercase there was increased social interaction. In ED2, al-though there was an increase in the means, this was notstatistically significant. The absence of this effect canbe attributed to waiting room layout, which was more in-timate and promoted conversation, thus the mean talking/person ratio was higher to start with. Increase in sociali-zation can be considered to be an indicator of positivemood. There is a significant amount of literature on theimportance of social support (frequent or prolonged con-tact with supportive friends and family) in health care set-tings (24). In the ED, positive interaction with the staffhas been found to be important (3,7,8,25). This articleposes the question of whether interaction with others inthe waiting room could also serve as social support, andif visual art can facilitate this interaction.
No significant difference in the distraction activities(cell phone usage, TV viewing, reading) was found inED1. In ED2, a reduction in TV viewing and an increasein looking out the window was found, which can be ex-plained by the reduction in the number of active TVscreens showing regular programming.
Finally, there was a significant decrease in the averagenoise level at both sites. Whereas the difference in noiselevels can be attributed to the difference in the number ofTVs with sound in ED2, this was not the case in ED1,where there were two screens showing regular TVprogramming at the same volume both pre and postintervention.
Previous studies argue that noise levels contribute topatient stress. We argue here that patient stress can con-tribute to noise levels. Is it possible that a more pleasantenvironment with carefully researched visual distractionsthat reduce restlessness in patients and contribute to calmbehavior can also contribute to a reduction in ambientnoise levels? Noise was not the emphasis of this study,but would be very interesting to investigate further. Anec-dotal comments from staff on the floor in ED1 reiteratedthe findings with the noise meter, making comments thatthe EDwas ‘‘quieter,’’ even though no activemeasures fornoise control had been taken.
Limitations and Future Directions
Due to the high stress and the high traffic in the ED, it wasnot feasible to interview the patients and visitors directlyto ask them about the artwork; a critical qualitative com-
ponent to the study is therefore missing. Patient satisfac-tion scores were initially identified as a metric totriangulate the data. Unfortunately, due to various opera-tional changes that took place in the hospitals during thistime, it was not possible to isolate the impact of the art in-tervention on satisfaction scores. Moreover, a patientwaits in multiple areas of the hospital, and the survey in-strument typically asks about the overall waiting experi-ence, including the waiting experience in examinationrooms and pre-procedure rooms; this was outside thescope of this study. In a follow-up study, customized sur-vey questions about the main waiting area and multivar-iate analyses of patient satisfaction data could beattempted.
CONCLUSIONS
In this article we have shown, through systematic behav-ioral observation, that providing an evidence-based posi-tive distraction can impact patient experience by reducingrestlessness (which can be an indicator of patient anxietyand stress), reducing people-watching (which has impli-cations for privacy), and increasing socializing (whichcould improve social support). Using positive distractionslike nature-based video and still art, instead of loud TVs,can also help to bring down the noise level and improvepatients’ mood, which has implications for patient andstaff satisfaction.We can therefore conclude that a simplevisual intervention, like still and video art, can improvethe patient waiting experience in the ED.
Acknowledgments—We would like to thank the Center forHealth Design for the Research Coalition grant that supportedthis project. We would also like to thank Kathy Hathorn,American Art Resources, for in-house support for the project.
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Impact of Visual Art in the ED 181
ARTICLE SUMMARY
1. Why is this topic important?As waiting times increase in emergency departments(EDs) across the country, improving patient experiencehas become a priority.2. What does this study attempt to show?This study attempts to show that visual art can be a simpleand cost-effective intervention to improve the patient ex-perience in the ED, and can have a direct impact on patientbehavior.3. What are the key findings?Introducing visual art in two EDwaiting rooms resulted inreduced restlessness and people-watching in the patients,as well as a reduction in overall noise levels.4. How is patient care impacted?Patient care potentially would be impacted by the im-proved mood of the patients, which results in lower stresslevels for both patients and staff.
