David L Cheung|Julie Sahrmann|Alvan Nzewuihe|Joseph Espiritu
No-show rates to a sleep clinic: drivers and determinants
Saint Louis University|Saint Louis University|Saint Louis University|Saint Louis University
free accessscientific investigationsnoshow rates sleep clinic drivers determinants david l cheung bs julie sahrmann alvan nzewuihe md mrcpi joseph r espiritu md msph david l cheung bs search papers author julie sahrmann search papers author alvan nzewuihe md mrcpi search papers author joseph r espiritu md msph search papers author published onlineseptember 15 2020httpsdoiorg105664jcsm8578cited by1sectionsabstractpdf shareshare onfacebooktwitterlinkedinredditemail toolsadd favoritesdownload citationstrack citations aboutabstractstudy objectivesattendance sleep clinic appointments imperative diagnose sleeprelated disorders offer appropriate treatment part quality assurance program assessed predictors noshow rates sleep clinic hypothesize noshow rates predicted demographics appointment type new vs established timing insurance statusmethodswe performed 10month retrospective chart review patients scheduled saint louis universitys slucare sleep disorders center multivariable logistic regression used determine factors independently associated noshowresultsa total 2532 clinical visits reviewed overall noshow rate 212 factors associated higher incidence noshow rates included younger age 1740 years 215 4164 years 235 65 years 140 p 0001 appointment type new 305 vs established 183 p 0001 insurance status insurance 246 vs public 226 vs private 159 p 0001 multivariable logistic regression confirmed independent association noshow age 40 years adjusted odds ratio 172 95 confidence interval 144 220 new patient status adjusted odds ratio 178 95 confidence interval 144 220 absence health insurance adjusted odds ratio 162 95 confidence interval 124 211 sex appointment time day week season significantly influence noshow ratesconclusionsindependent predictors noshow appointments included younger age new patient status lack health insurance findings aid future efforts identify patients high predictors nonadherence studies needed develop methods decrease noshow rates highrisk appointments identifiedcitationcheung dl sahrmann j nzewuihe espiritu jr noshow rates sleep clinic drivers determinants j clin sleep med 202016915171521brief summarycurrent knowledgestudy rationale approximately 5070 million americans chronic sleep wakefulness disorders leading impairments daily activities increased risk obesity heart diseases depression type 2 diabetes however treat patients provide resources sleep imperative present appointmentsstudy impact goal study highlight possible driving determining factors noshow rates sleep clinics clinics use data implement new initiatives decrease noshow rates clinicsintroductionapproximately 5070 million americans chronic sleep wakefulness disorders leading impairments daily activities increased risk obesity heart diseases depression type 2 diabetes1 prevalence incidence treatment predictors pathophysiology outcomes sleep wakefulness disorders well known however treat patients provide resources sleep imperative present appointments although important aspect care research noshow rates among sleep clinics limitedfrom financial standpoint noshow rates increase cost health care study looking 10 regional hospitals found average noshow rate 188 larger rates occurring subspecialty clinics2 equated cost 196 per patient 20082 part quality assurance improvement evaluating noshow rates maximize access care reduce cost health careprevious studies focused predictors noshow rates evaluated variety factors prospective study pain treatment center john hopkins hospital odonkor et al3 revealed higher noshow rates among racial ethnic minorities among people younger age 65 among people medicare medicaid snowy rainy days among lower back pain headaches least one previous pain provider although study offers general factors noshow rates subspecialty clinic study limited obtained data 68 minority population within inner city include variables marital status employment insurance opioid use location residence3 similar prospective study breast clinic phoenix arizona knolhoff et al4 found patients limited health literacy current smokers higher rates noshow study however limited population 982 female widely diverse sociodemographic characteristics race language education employment insurance4 retrospective study safetynet gastroenterology endoscopy suite university florida jacksonville shuja et al5 found predictors higher noshow rates unpartnered single patients african americans noncommercial insurance providers contrast found lower noshow rate patients requiring surveillance colonoscopy vs initial screening5 study limited however never factored level education symptom acuity history psychiatric illness substance abuse differences clinicdriven cancellations patientdriven cancellations5 studies also focused providers side scheduling appointments pulmonary gastroenterology clinic phoenix childrens hospital drewek et al6 found appointments scheduled later 30 days higher chance noshow compared appointments earlier 30 daysbased literature wide variety factors lead noshow rates subspecialty clinics none looked attendance sleep clinics specifically study hypothesize certain demographic characteristics appointment type time health insurance coverage may associated higher noshow rates address predictors noshow rates sleep disorders clinic assessed effect sex age type time appointment day week season year type insurance purpose study identify factors associated higher noshow rates sleep clinic part quality assurance improvement addressing factors may predict patient noshow hope sleep clinics could optimize scheduling practices proactive patients suggest high risk missed appointments ensure quality caremethodsour study reviewed approved saint louis university institutional review board december 11 2018 need informed consent waived project constitute human subjects researchthis study conducted slucare sleep disorders center saint louis university st louis mo slucare sleep disorders center multidisciplinary academic sleep facility accredited american academy sleep medicine vast majority 80 patients referred primary care providers suspected sleeprelated breathing disorderswe performed retrospective study course 10 months months july 2017 april 2018 study collected limited deidentified dataset patients scheduled appointment sleep center patients scheduled clinic included study exclusions data total 2532 patients screened electronic health record system epic epic system corporation released 2019 epic systems corporation verona wi patients contacted purposes studythe independent variables collected included patients sex male female age category 1740 4164 65 years status appointment new vs established clinic visit time appointment pm day week appointment monday friday season year appointment julysummer octoberfall januarywinter aprilspring health insurance coverage none public private data clinic visits collected months july 2018 october 2018 january 2019 april 2019 represent distinct seasonthe dependent variable analysis whether appointment status completed show missed noshow appointment completed patient arrived clinic scheduled clinic visit appointment deemed noshow either patient failed show scheduled appointment time canceled appointment enhance patient show rates automated reminder calls made 7 days appointment response required patients addition practice also mandates medical assistants call patients telephone day day appointment respectively confirm attendancestatistical analysis2 bivariate analyses conducted determine crude association independent variables outcome show vs noshow multivariate binary logistic regression model fitted determine whether significant independent variables determined univariate analyses associated outcome show vs noshow spss ibm corp released 2015 ibm spss windows version 220 armonk ny used statistical analysisresultsour study identified 2532 patients overall noshow rate 2120 months july october january april comparable overall noshow rate 20 months july april included determinants noshow age status appointment new vs established type insurance played role determining whether patient would show appointment terms demographic characteristics age category p 0001 sex p 351 significantly associated noshow rates patients 65 years less likely miss clinic appointment significantly lower noshow rate 14 compared 1740 years age 215 4164 years age 235 table 1 figure 1 status appointment new patients never clinic significantly higher noshow rate people returned subsequent clinic visit p 0001 course 10month study 187 613 305 new patients failed attend appointments compared 351 1919 183 established patients previously received care clinic table 1 figure 1 terms health care coverage patients without health insurance significantly higher noshow rates either public private health insurance p 0001 course 10month study 255 1036 246 people insurance show appointment compared 130 822 patients 158 private health insurance 152 674 226 public health insurance table 1 aspects appointment sex p 351 day week p 375 time day p 737 season appointment p 106 associated appointment attendance table 1 multivariable binary logistic regression analysis incorporating significant univariate independent variables age category type appointment health insurance coverage confirmed younger age category 1740 years new clinic visit lack health insurance independently associated noshow table 2 terms age adjusted analysis showed patients 1740 years age 172 95 confidence interval 123 240 times likely miss clinic appointment 65 years age table 2 contrast 4164 years age similar clinic attendance 65 years table 2 new patient 178 95 confidence interval 144 220 times likely show initial clinic visit established patient coming subsequent clinic visit patients without health insurance 162 95 confidence interval 124 211 times likely show clinic visit private public health insurancetable 1 clinical attendance based demographic characteristics status clinic visit type health care coverage temporal characteristics appointmentvariablenoshow n show n page yr0001 1740137 215501 785 4164334 2351089 765 6566 140405 860sex351 female309 2191103 781 male228 204892 796status0001 established clinic visit351 1831568 817 new clinic visit187 305426 695insurance0001 public152 226521 774 private130 159689 841 none255 246781 753day276 monday134 220476 780 tuesday134 242419 758 wednesday115 201456 799 thursday90 191380 809 friday54 206208 794time737 am214 218767 782 pm323 2081228 792season442 summer129 193540 807 fall129 209489 791 winter144 221508 779 spring135 228458 772figure 1 appointment attendance based appointment type age category health insurancethe bar graph illustrates appointment attendance number patients show vs noshow based appointment type new vs established age category type health insurance coverage none public private new patients 196 times likely show established patients age 35 3664 65 years appointment attendance show vs noshow younger patients 35 years 153 times likely show older patients aged 3664 65 years type insurance coverage appointment attendance patients health insurance 155 times likely show clinic visit private public health insurance statistical analysis showed p 0001download figuretable 2 multivariate logistic regression predicting noshow based age category status appointment health care coverageindependent variableadjusted odds ratio 95 confidence intervalage yr 1740172 123 240 4164087 069 109 65100 referencestatus appointment established100 reference new178 144 220health care coverage public100 reference private088 070 112 none162 124 211discussionthis retrospective study estimated approximately 1 5 adult patients show appointment sleep disorders clinic universitybased specialty clinic located within major city midwestern united states reviewed multispecialty academic practice quality assurance report july 2017 july 2018 pulmonary clinic situated different geographic location doctors office building sleep disorders center listed noshow rate 222 rate similar sleep disorders clinic kosydor written communication september 2018 noshow rate also tracks closely overall 188 noshow rate reported subspecialty clinics regional health care centers united states2our study found higher noshow rates patients younger age 40 years new sleep disorder clinic without health insurance potential reasons younger patients missing appointments include employment demands lack sense urgency although unable ascertain factors study adults younger 40 years may lower level jobs allow flexible medical leave compared older adults 65 years likely retired younger patients may also perceive healthier less vulnerable complications sleep disorders feel urgency seek medical evaluation furthermore new patients may looking different sleep centers perhaps closer homes places work feel obligated stick health care system provider also understandable patients without health insurance miss appointments exorbitant cost health care financial burden clinic visits data align similar studies noshow rates different types clinics study odonkor et al3 mirrored results higher noshow rates patients younger limited health care coverage study shuja et al5 also found patients noncommercial insurance eg medicare medicaid city contract statistically higher noshow rates commercial insurance although study shuja et al5 investigate patients without insurance findings corroborate poor noshow rates individuals struggle obtain health insurancethere several strengths study although studies looked determinants noshow rates none ever looked determinants affect sleep disorder clinic sleep disorder clinic involve timeextensive procedures sleep studies noshows large impact poor workflow wasted resources study offers insight combating noshow rates improving patient outcomes large sample provided statistical power detect statistically significant differences allowed us conduct multivariable logistic regression analysis potentially significant independent variablesthere also several limitations study retrospective study based abstraction electronic health records finite number independent variables collected determine factors associated patient noshow hence might unknown confounding determinants included analysis one potential confounder appointment lag time may result memory decay scheduled appointment passage time7 average appointment lag time sleep disorders clinic study period august 2017 august 2018 198 days kosydor written communication september 2018 comparison drewek et al6 observed higher noshow rates pulmonary gastrointestinal pediatric clinics appointment lag time longer 30 days thus unlikely 20day appointment lag time adult sleep disorders clinic could influenced noshow rate meanwhile none patients contacted clinic visit discern reason noshow data never distinguished cancellations noshow thus results determine potential psychosocial mechanisms behind noshow addition results cannot broadly generalized sleep centers serve populations demographic distributions different however findings may prove similar certain academic sleep centers located midwestern urban areas cover diversified underserved populations another purported confounder might influence show rate underlying diagnosis queried frequency billing diagnoses center 6 months study time period found vast majority patients evaluated sleeprelated breathing disorders ie obstructive sleep apnea 7512 central sleep apnea 340 sleeprelated hypoxemiahypoventilation 1 whereas remainder nonrespiratoryrelated sleep disorders 20 r c craig written communication november 2018 much larger proportion obstructive sleep apnea diagnosis compared diagnoses cohort would allowed us statistically compare noshow rates among different sleep disorder diagnoses source reason referral eg patient selfreferral sleepiness referral spouse patients snoring referral surgeon preoperative evaluation primary care provider vs specialist referral concern sleeprelated risk factors might also played role show rates expeditiously ascertained electronic health record finally although overall noshow rate calculated period 10 months data months july october january april data months included could underestimate overestimate noshow rate per seasonfurther studies explore reasons behind noshow rates sleep disorders clinics purported reasons include long appointment lag times long wait times patient satisfaction previous clinic visits aversion treatment recommendations eg continuous positive airway pressure therapy comorbid medical conditions educational attainment employment status job schedule availability eveningweekend hours alternative clinic locations additionally modifications policies made address determinants noshow practice example patients health insurance referred clinic staff billing department discuss professional fees payment plans health care provider systems also use social workers assist patient applying governmentsubsidized health insurance eg medicaid reduce disparities health care access underserved groups newly referred patients offered additional appointment reminders via telephone call text messaging email decrease chance noshow moreover practices long appointment lag time new patients could given priority slots appointment schedule quickly address sleep complaints alternative approach improving access use virtual clinic visits via telemedicine telemedicine visits may improve show rates obviating need transportation sleep disorders clinic contrast technological access telemedicine equipment may limited particularly underserved groups systematically investigating determinants noshow implementing evidencebased approaches mitigate factors enhance health care participation thereby improve healthrelated outcomes patients sleep disordersconclusionsindependent predictors noshow appointments included younger age new patient status lack health insurance findings aid future efforts identify patients high predictors nonadherence studies needed develop methods decrease noshow rates highrisk appointments identifieddisclosure statementall authors seen approved manuscript work study performed slucare sleep disorders center saint louis university school medicine st louis mo authors report conflicts interestreferences1 colten hr altevogt bm eds sleep disorders sleep deprivation unmet public health problem washington dc national academies press 2006 google scholar2 kheirkhah p feng q travis lm tavakolitabasi sharafkhaneh prevalence predictors economic consequences noshows bmc health serv res 201616113 httpsdoiorg101186s129130151243z crossrefgoogle scholar3 odonkor ca christiansen chen et al factors associated missed appointments academic pain treatment center prospective yearlong longitudinal study anesth analg 20171252562570 httpsdoiorg101213ane0000000000001794 crossrefgoogle scholar4 knolhoff jb djenic b hsu ch bouton komenaka ik missed appointments breast clinic patientrelated factors j med sci 20163524337342 httpsdoiorg101016jamjms201607003 crossrefgoogle scholar5 shuja harris c aldridge p malespin de melo sw predictors noshow rate gi endoscopy suite safety net academic medical center j clin gastroenterol 20195312933 httpsdoiorg101097mcg0000000000000928 crossrefgoogle scholar6 drewek r mirea l adelson pd lead time appointment noshow rates new followup patients ambulatory clinic health care manag frederick 201736149 crossrefgoogle scholar7 ricker tj vergauwe e cowan n decay theory immediate memory brown 1958 today 2014 q j exp psychol hove 2016691019691995 httpsdoiorg101080174702182014914546 crossrefgoogle scholar previous article next article figuresreferencesrelateddetailscited factors effective telemedicine visits covid19 pandemic cohort studygmunder k ruiz j franceschi suarez jmir medical informatics 10219627977 vol 9 8 e27977 volume 16 issue 9 september 15 2020issn print 15509389issn online 15509397frequency monthly metrics history submitted publicationmarch 19 2020submitted final revised formmay 7 2020accepted publicationmay 12 2020published onlineseptember 15 2020 information 2020 american academy sleep medicinekeywordsdeterminantsleep disordersleep clinicretrospectivenoshowappointments