ArticlePDF Available

Use of the internet for health information: United States, 2009

Authors:

Abstract

Research has shown that 74% of all U.S. adults use the Internet, and 61% have looked for health or medical information on the Internet. Additionally, 49% have accessed a website that provides information about a specific medical condition or problem. In 2009, the National Health Interview Survey (NHIS) became the first nationally representative household survey to collect data on the use of health information technology when the U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation sponsored 10 questions that asked about use of the Internet to look up health information, refill a prescription, schedule a medical appointment, learn about health topics in online chat groups, and e-mail a health care provider. This report provides estimates, using 2009 NHIS data, about adult use of the Internet for health information in the past 12 months, by selected sociodemographic characteristics.
NCHS Data Brief No. 66 July 2011
U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Disease Control and Prevention
National Center for Health Statistics
Use of the Internet for Health Information: United States, 2009
Robin A. Cohen, Ph.D.; and Patricia F. Adams
Key ndings
Data from the National
Health Interview Survey,
2009
• Amongadultsaged18and
over,womenweremorelikely
thanmentohaveusedthe
Internetforhealthinformation.
• Amongadultsaged18–64,
non-Hispanicwhitepersons
werealmosttwiceaslikely
asHispanicpersonstohave
usedtheInternetforhealth
information.
• Adultsaged18–64with
higherincomesweremore
likelytohaveusedtheInternet
forhealthinformationthan
adultswithlowerincomes.
• Employedadultsaged18–64
weremorelikelythanadults
whowereunemployedornotin
theworkforcetohaveusedthe
Internetforhealthinformation.
Researchhasshownthat74%ofallU.S.adultsusetheInternet,and61%have
lookedforhealthormedicalinformationontheInternet(1).Additionally,49%
haveaccessedawebsitethatprovidesinformationaboutaspecicmedical
conditionorproblem.In2009,theNationalHealthInterviewSurvey(NHIS)
becametherstnationallyrepresentativehouseholdsurveytocollectdataon
theuseofhealthinformationtechnologywhentheU.S.DepartmentofHealth
andHumanServices,OfceoftheAssistantSecretaryforPlanningand
Evaluationsponsored10questionsthataskedaboutuseoftheInternettolook
uphealthinformation,rellaprescription,scheduleamedicalappointment,
learnabouthealthtopicsinonlinechatgroups,ande-mailahealthcare
provider.Thisreportprovidesestimates,using2009NHISdata,aboutadult
useoftheInternetforhealthinformationinthepast12months,byselected
sociodemographiccharacteristics.
Keywords: health information technology, Internet, National Health Interview
Survey
Among adults aged 18 and over, women were more likely
than men to have used the Internet for health information.





DQGRYHU±±±±DQGRYHU

 

 

 

 

 



)HPDOH
0DOH
%RWKVH[HV
)LJXUH3HUFHQWDJHRIDGXOWVDJHGDQGRYHUZKRLQWKHSDVWPRQWKVORRNHGXSKHDOWK
LQIRUPDWLRQRQWKH,QWHUQHWE\VH[DQGDJH8QLWHG6WDWHV
$JHLQ\HDUV
3HUFHQW
127('DWDDUHEDVHGRQKRXVHKROGLQWHUYLHZVRIDVDPSOHRIWKHFLYLOLDQQRQLQVWLWXWLRQDOL]HGSRSXODWLRQ
6285&(&'&1&+61DWLRQDO+HDOWK,QWHUYLHZ6XUYH\6DPSOH$GXOWFRPSRQHQW
NCHS Data Brief No. 66 July 2011
■  2  ■
WomenweremorelikelythanmentohaveusedtheInternetforhealthinformationinthepast12
monthsforeachagegroupexcept65andover(Figure1).
ThepercentageofadultswhousedtheInternetforhealthinformationwashighestamongwomen
aged25–34(65.8%)andlowestamongadultsaged65andover(under25%).
Among adults aged 18–64, non-Hispanic white persons were almost
twice as likely as Hispanic persons to have used the Internet for health
information.
UseoftheInternetbyadultsaged18–64forhealthinformationinthepast12monthswashighest
foradultswhowerenon-Hispanicwhite(57.3%),followedbynon-HispanicAsian(47.8%),non-
Hispanicblack(38.3%),andHispanic(28.8%)(Figure2).
127(6+LVSDQLFSHUVRQVPD\EHRIDQ\UDFH1RQ+LVSDQLFSHUVRQVRIRWKHUUDFHVRURIPL[HGUDFHDUHQRWVKRZQ'DWDDUHEDVHGRQKRXVHKROGLQWHUYLHZVRID
VDPSOHRIWKHFLYLOLDQQRQLQVWLWXWLRQDOL]HGSRSXODWLRQ
6285&(&'&1&+61DWLRQDO+HDOWK,QWHUYLHZ6XUYH\6DPSOH$GXOWFRPSRQHQW
)LJXUH3HUFHQWDJHRIDGXOWVDJHG±ZKRLQWKHSDVWPRQWKVORRNHGXSKHDOWKLQIRUPDWLRQRQWKH,QWHUQHWE\
UDFHDQGHWKQLFLW\8QLWHG6WDWHV









3HUFHQW
1RQ+LVSDQLF$VLDQ1RQ+LVSDQLFEODFN1RQ+LVSDQLFZKLWH+LVSDQLF
NCHS Data Brief No. 66 July 2011
■  3  ■
Among adults aged 25–64, higher education was associated with increased
use of the Internet for health information.
Amongadultsaged25–64,73.8%ofpersonswithatleastacollegedegreeusedtheInternetfor
healthinformationinthepast12months,whileonly13.8%ofthosewithlessthanahighschool
educationdidso(Figure3).
 







&ROOHJHJUDGXDWH6RPHFROOHJH+LJKVFKRROGLSORPD
RU*('
/HVVWKDQKLJKVFKRRO
3HUFHQW
127(6*('LV*HQHUDO(GXFDWLRQDO'HYHORSPHQWKLJKVFKRROHTXLYDOHQF\GLSORPD'DWDDUHEDVHGRQKRXVHKROGLQWHUYLHZVRIDVDPSOHRIWKHFLYLOLDQ
QRQLQVWLWXWLRQDOL]HGSRSXODWLRQ
6285&(&'&1&+61DWLRQDO+HDOWK,QWHUYLHZ6XUYH\6DPSOH$GXOWFRPSRQHQW
)LJXUH3HUFHQWDJHRIDGXOWVDJHG±ZKRLQWKHSDVWPRQWKVORRNHGXSKHDOWKLQIRUPDWLRQRQWKH,QWHUQHWE\
HGXFDWLRQVWDWXV8QLWHG6WDWHV
Adults aged 18–64 with higher incomes were more likely to have used the
Internet for health information than adults with lower incomes.
Adultsaged18–64withincomesatorabove300%ofthefederalpovertylevel(FPL)weremore
thantwiceaslikely(63.4%)tohaveusedtheInternetforhealthinformationinthepast12months
comparedwithadultswithincomeslessthan100%oftheFPL(28.9%)(Figure4).
127(6)3/LVIHGHUDOSRYHUW\OHYHO'DWDDUHEDVHGRQKRXVHKROGLQWHUYLHZVRIDVDPSOHRIWKHFLYLOLDQQRQLQVWLWXWLRQDOL]HGSRSXODWLRQ
6285&(&'&1&+61DWLRQDO+HDOWK,QWHUYLHZ6XUYH\6DPSOH$GXOWFRPSRQHQW
)LJXUH3HUFHQWDJHRIDGXOWVDJHG±ZKRLQWKHSDVWPRQWKVORRNHGXSKHDOWKLQIRUPDWLRQRQWKH,QWHUQHWE\
SRYHUW\VWDWXV8QLWHG6WDWHV
)3/RUPRUHWROHVV
WKDQ)3/
WROHVV
WKDQ)3/
/HVVWKDQ)3/









3HUFHQW
NCHS Data Brief No. 66 July 2011
■  4  ■
Employed adults aged 18–64 were more likely than adults who were
unemployed or not in the workforce to have used the Internet for health
information.
Morethanone-half(53.4%)ofemployedadultsaged18–64usedtheInternetforhealth
informationinthepast12monthscomparedwith40.9%ofunemployedadultsand42.5%of
adultsnotintheworkforce(Figure5).
127('DWDDUHEDVHGRQKRXVHKROGLQWHUYLHZVRIDVDPSOHRIWKHFLYLOLDQQRQLQVWLWXWLRQDOL]HGSRSXODWLRQ
6285&(&'&1&+61DWLRQDO+HDOWK,QWHUYLHZ6XUYH\6DPSOH$GXOWFRPSRQHQW
)LJXUH3HUFHQWDJHRIDGXOWVDJHG±ZKRLQWKHSDVWPRQWKVORRNHGXSKHDOWKLQIRUPDWLRQRQWKH,QWHUQHWE\
HPSOR\PHQWVWDWXV8QLWHG6WDWHV





3HUFHQW



1RWLQZRUNIRUFH8QHPSOR\HG(PSOR\HG
Using the Internet for health information was related to health insurance
status.
Amongadultsaged18–64,58.7%ofthosewithprivatehealthinsurancecoverageusedthe
Internetforhealthinformationinthepast12monthscomparedwith31.3%ofthoseonMedicaid
and33.3%ofthosewithnoinsurancecoverage(Figure6).
127('DWDDUHEDVHGRQKRXVHKROGLQWHUYLHZVRIDVDPSOHRIWKHFLYLOLDQQRQLQVWLWXWLRQDOL]HGSRSXODWLRQ
6285&(&'&1&+61DWLRQDO+HDOWK,QWHUYLHZ6XUYH\6DPSOH$GXOWFRPSRQHQW
)LJXUH3HUFHQWDJHRIDGXOWVDJHG±ZKRLQWKHSDVWPRQWKVORRNHGXSKHDOWKLQIRUPDWLRQRQWKH,QWHUQHWE\
KHDOWKLQVXUDQFHFRYHUDJHVWDWXV8QLWHG6WDWHV





8QLQVXUHG2WKHUFRYHUDJH0HGLFDLG3ULYDWH
3HUFHQW




NCHS Data Brief No. 66 July 2011
■  5  ■
Summary
Sociodemographicandsocioeconomicfactorswereassociatedwithadultswhohadusedthe
Internettolookuphealthinformation.GreateruseoftheInternetforhealthinformationin
thepast12monthsamongadultswasassociatedwithbeingages25–44,non-Hispanicwhite,
employed,collegeeducated,withincomeatorabove300%oftheFPL,andhavingprivatehealth
insurance.
Theactiveinvolvementofconsumersinmanagingtheirhealthcareincludesactivitiessuchas
useofcomputerstoaccess,retrieve,store,orsharehealthcareinformation.Forconsumersthis
mayincludeusingtheInternettolookuphealthinformation,usinge-mailortextmessagingto
communicatewithhealthcareprovidersorpharmacies,andhavinganelectronichealthrecord.
AsthepercentageofadultsintheUnitedStatesusingtheInternetcontinuestogrow,theInternet
asasourceofhealthinformationforconsumersmaybecomeincreasinglyimportant.Although
useoftheInternethasthepotentialtoimproveconsumerhealthbyfacilitatingcommunication
betweenprovidersandpatientsandamongproviders,previousresearchhasfoundthatmany
consumersareconcernedaboutsecurityandcondentialityissuesrelatedtoschedulingmedical
appointmentsoraccessingpersonalhealthrecordsonline(2).
Denitions
LookeduphealthinformationontheInternetinthepast12months:Basedonapositiveresponse
tobothofthefollowingquestions:“Haveyoueverusedcomputersforanyofthefollowing?…
LookeduphealthinformationontheInternet”and“Didyoulookuphealthinformationonthe
Internetinthepast12months?”Adultswhoanswered“Refused”or“Don’tknow”foreitherof
thesequestionswereexcludedfromthedenominatorforthisanalysis.
Povertystatusandpercentageofpovertylevel:Basedonfamilyincome,familysize,andthe
numberofchildreninthefamily,and,forfamilieswithtwoorfeweradults,ontheageofthe
adultsinthefamily.ThepovertylevelisbasedondenitionsoriginallydevelopedbytheSocial
SecurityAdministration.TheseincludeasetofincomethresholdsdeningtheFPLthatvary
byfamilysizeandcomposition.Familiesorindividualswithincomesbelowtheirappropriate
thresholdsareclassiedasbelowthepovertylevel.Thesethresholdsareupdatedannuallybythe
U.S.CensusBureautoreectchangesintheConsumerPriceIndexforallurbanconsumers.For
furtherinformation,visithttp://www.census.gov/hhes/www/poverty/poverty.html.Estimatesby
povertystatusarebasedonbothreportedandimputedfamilyincome.Familyincomeinformation
wascompletelymissingfor5%ofpersons,andwasonlyreportedinbroadcategoriesforan
additional19%ofpersonsin2009.Therefore,familyincomewasimputedfor24%ofpersonsin
2009usingNHISimputedincomeles(3).NotethatNHISasksrespondentsabouttheirpersonal
earningsandfamilyincomeforthepreviouscalendaryear;therefore,U.S.CensusBureau
povertythresholdsforthepreviouscalendaryearshouldbeusedwhencalculatingpovertyratios
forthecurrentNHISsurveyyear.Forexample,thepovertyratiosinthe2009NHISdatales
werecalculatedusingtheU.S.CensusBureau’s2008povertythresholds(4).
Healthinsurancecoverage:Denedbroadlytoincludebothpublicandprivatepayerswhocover
medicalexpendituresincurredbyadenedpopulationinavarietyofsettings.Thisincludes
personscoveredbyprivatehealthinsurance,whetherofferedthroughemploymentorpurchased
individually,andpersonscoveredbypublicprogramssuchasMedicare,Medicaid,Children’s
NCHS Data Brief No. 66 July 2011
■  6  ■
HealthInsuranceProgram(CHIP),andotherstate-sponsoredprograms.PersonswithonlyIndian
HealthService(IHS)coverageorhavingonlyaprivateplanthatpaidforonetypeofservicesuch
asaccidentsordentalcarewerenotconsideredtobecoveredbyhealthinsurance.Inthisreport,
coverageismeasuredonlyonthedayoftheNHISinterview.
Privateinsurance:Indicatedwhenrespondentsreportthattheywerecoveredbyprivatehealth
insurancethroughanemployer,union,orindividualpurchase.Privatehealthinsuranceincludes
managedcaresuchashealthmaintenanceorganizationsanddoesnotincludemilitaryhealth
plans.
Medicaid:IndicatedwhenrespondentsreportthattheywerecoveredbyMedicaid.Individuals
werealsoconsideredcoveredbyMedicaidiftheyreportedcoveragebyCHIPorotherstate-
sponsoredplans.Inthisanalysis,healthinsurancecategoriesarehierarchical,andadultscovered
bybothprivateinsuranceandMedicaidwereconsideredtohaveprivateinsurance.
Othercoverage:IncludespersonswhodonothaveprivatecoverageorMedicaid(orotherpublic
coverage),butwhohaveanytypeofmilitaryhealthplan(includesVA,TRICARE,andCHAMP–
VA)orMedicare.Thiscategoryalsoincludespersonswhoarecoveredbyothergovernment
programs.
Uninsured:Indicatedwhenrespondentsreportthattheydidnothavecoverageunderprivate
healthinsurance,Medicare,Medicaid,CHIP,anotherstate-sponsoredhealthplan,other
government-sponsoredprograms,oramilitaryhealthplan(TRICARE,VA,orCHAMP–VA).
ApersonwasalsodenedasuninsuredifheorshehadonlyIHScoverageoraprivateplanthat
paidforonetypeofservicesuchasaccidentsordentalcare.
Data source and methods
Datafromthe2009NHISwereusedforthisanalysis.NHISisconductedcontinuously
throughouttheyearbyinterviewersoftheU.S.CensusBureaufortheCentersforDisease
ControlandPrevention’s(CDC)NationalCenterforHealthStatistics(NCHS).NHIScollects
informationaboutthehealthandhealthcareoftheciviliannoninstitutionalizedU.S.population.
Interviewsareconductedinrespondents’homes,butfollowupstocompleteinterviewsmay
beconductedoverthetelephone.IntheFamilycomponentofthesurvey,familyrespondents
areaskedquestionsabouteveryoneinthefamily,includingquestionsabouthealthinsurance
coverage.QuestionsonuseoftheInternetarefromtheSampleAdultcomponent.In2009,
informationwascollectedonatotalof27,731personsaged18andoverfromtheSampleAdult
componentofthesurvey.ForfurtherinformationaboutNHIS,includingthequestionnaire,visit
http://www.cdc.gov/nchs/nhis.htm.
NHISisdesignedtoyieldasamplerepresentativeoftheciviliannoninstitutionalizedpopulation
oftheUnitedStates,andthisanalysisusesweightstoproducenationalestimates.Data-
weightingproceduresaredescribedinmoredetailelsewhere(5).Pointestimatesandestimates
ofcorrespondingvariancesforthisanalysiswerecalculatedusingSUDAANsoftware(6)to
accountforthecomplexsampledesignofNHIS.TheTaylorserieslinearizationmethodwas
chosenforvarianceestimation.Differencesbetweenpercentageswereevaluatedusingtwo-
sidedsignicancetestsatthe0.05level.Termssuchas“higherthan”and“lessthan”indicate
statisticallysignicantdifferences.Termssuchas“similar”and“nodifference”indicatethat
NCHS Data Brief No. 66 July 2011
■  7  ■
thestatisticsbeingcomparedwerenotsignicantlydifferent.Lackofcommentregardingthe
differencebetweenanytwostatisticsdoesnotnecessarilysuggestthatthedifferencewastested
andfoundtobenotsignicant.
About the authors
RobinA.CohenandPatriciaF.AdamsarewithCDC’sNCHS,DivisionofHealthInterview
Statistics.
References
1. FoxS,JonesS.ThesociallifeofInternetusers.Washington,DC:PewInternet&American
LifeProject.2009.
2. ThestateofhealthinformationtechnologyinCalifornia.Oakland,CA:CaliforniaHealthCare
Foundation.2008.
3. SchenkerN,RaghunathanTE,ChiuPL,etal.Multipleimputationoffamilyincomeand
personalearningsintheNationalHealthInterviewSurvey:Methodsandexamples.Available
from:http://www.cdc.gov/nchs/data/nhis/tecdoc.pdf.
4. DeNavas-WaltC,ProctorBD,SmithJC.Income,poverty,andhealthinsurancecoveragein
theUnitedStates:2008.U.S.CensusBureau.Currentpopulationreports,P60–236.Washington,
DC:U.S.GovernmentPrintingOfce.2009.Availablefrom:http://www.census.gov/
prod/2009pubs/p60-236.pdf.
5. BotmanSL,MooreTF,MoriarityCL,ParsonsVL.DesignandestimationfortheNational
HealthInterviewSurvey,1995–2004.NationalCenterforHealthStatistics.VitalHealthStat
2(130).2000.
6. ResearchTriangleInstitute.SUDAAN(Release10.0).ResearchTrianglePark,NC:Research
TriangleInstitute.2008.
NCHS Data Brief No. 66 July 2011
Suggested citation
CohenRA,AdamsPF.UseoftheInternet
forhealthinformation:UnitedStates,2009.
NCHSdatabrief,no66.Hyattsville,MD:
NationalCenterforHealthStatistics.2011.
Copyright Information
Allmaterialappearinginthisreportisin
thepublicdomainandmaybereproduced
orcopiedwithoutpermission;citationasto
source,however,isappreciated.
National Center for Health
Statistics
EdwardJ.Sondik,Ph.D.,Director
JenniferH.Madans,Ph.D.,Associate
Director for Science
Division of Health Interview Statistics
JaneF.Gentleman,Ph.D., Director
U.S.DEPARTMENTOF
HEALTH&HUMANSERVICES
CentersforDiseaseControlandPrevention
NationalCenterforHealthStatistics
3311ToledoRoad
Hyattsville,MD20782
OFFICIALBUSINESS
PENALTYFORPRIVATEUSE,$300
Toreceivethispublicationregularly,contactthe
NationalCenterforHealthStatisticsby
calling1–800–232–4636
E-mail:cdcinfo@cdc.gov
Internet:http://www.cdc.gov/nchs
FIRST CLASS MAIL
POSTAGE & FEES PAID
CDC/NCHS
PERMIT NO. G-284
ISSN 1941–4927 (Print ed.)
ISSN 1941–4935 (Online ed.)
CS224474
DHHSPublicationNo.(PHS)2011–1209
... In the last few years, the Internet has become one of the most popular and reliable platforms for accessing healthcare-related information. A survey by Cohen et al. 1 found that more than 65% of US adults use the Internet for performing several healthcare-related activities. Over the past 5 years, numerous surveys have highlighted an alarming population-to-doctor ratio in different countries, emphasizing the urgent need for improvements in healthcare systems. ...
Article
Full-text available
With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients becomes challenging. In this work, we build a diagnosis assistant to assist doctors, which identifies diseases based on patient–doctor interaction. During diagnosis, doctors utilize both symptomatology knowledge and diagnostic experience to identify diseases accurately and efficiently. Inspired by this, we investigate the role of medical knowledge in disease diagnosis through doctor–patient interaction. We propose a two-channel, knowledge-infused, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient–doctor communication using a transformer-based encoder, while the other creates an embedding of symptom-disease using a graph attention network (GAT). In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification. Furthermore, we first develop an empathetic conversational medical corpus comprising conversations between patients and doctors, annotated with intent and symptoms information. The proposed model demonstrates a significant improvement over the existing state-of-the-art models, establishing the crucial roles of (a) a doctor’s effort for additional symptom extraction (in addition to patient self-report) and (b) infusing medical knowledge in identifying diseases effectively. Many times, patients also show their medical conditions, which acts as crucial evidence in diagnosis. Therefore, integrating visual sensory information would represent an effective avenue for enhancing the capabilities of diagnostic assistants.
... In the last few years, the Internet has become one of the most popular and reliable platforms for accessing healthcare-related information. A survey by Cohen et al. 1 found that more than 65% of US adults use the Internet for performing several healthcare-related activities. Over the past five years, numerous surveys have highlighted an alarming population-to-doctor ratio in different countries, emphasizing the urgent need for improvements in healthcare systems. ...
Preprint
Full-text available
With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients becomes challenging. In this work, we build a diagnosis assistant to assist doctors, which identifies diseases based on patient-doctor interaction. During diagnosis, doctors utilize both symptomatology knowledge and diagnostic experience to identify diseases accurately and efficiently. Inspired by this, we investigate the role of medical knowledge in disease diagnosis through doctor-patient interaction. We propose a two-channel, knowledge-infused, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient-doctor communication using a transformer-based encoder, while the other creates an embedding of symptom-disease using a graph attention network (GAT). In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification. Furthermore, we first develop an empathetic conversational medical corpus comprising conversations between patients and doctors, annotated with intent and symptoms information. The proposed model demonstrates a significant improvement over the existing state-of-the-art models, establishing the crucial roles of (a) a doctor's effort for additional symptom extraction (in addition to patient self-report) and (b) infusing medical knowledge in identifying diseases effectively. Many times, patients also show their medical conditions, which acts as crucial evidence in diagnosis. Therefore, integrating visual sensory information would represent an effective avenue for enhancing the capabilities of diagnostic assistants.
... [5] It is estimated that over half of adults in the United States use the Internet to acquire health information and advice. [6,7] We postulate that patients are likely (or soon will be) using chatbots for this purpose as well. ...
Article
Full-text available
Background The study compared the readability, grade level, understandability, actionability, and accuracy of standard patient educational material against artificial intelligence chatbot-derived patient educational material regarding cirrhosis. Methods An identical standardized phrase was used to generate patient educational materials on cirrhosis from 4 large language model-derived chatbots (ChatGPT, DocsGPT, Google Bard, and Bing Chat), and the outputs were compared against a pre-existing human-derived educational material (Epic). Objective scores for readability and grade level were determined using Flesch-Kincaid and Simple Measure of Gobbledygook scoring systems. 14 patients/caregivers and 8 transplant hepatologists were blinded and independently scored the materials on understandability and actionability and indicated whether they believed the material was human or artificial intelligence-generated. Understandability and actionability were determined using the Patient Education Materials Assessment Tool for Printable Materials. Transplant hepatologists also provided medical accuracy scores. Results Most educational materials scored similarly in readability and grade level but were above the desired sixth-grade reading level. All educational materials were deemed understandable by both groups, while only the human-derived educational material (Epic) was considered actionable by both groups. No significant difference in perceived actionability or understandability among the educational materials was identified. Both groups poorly identified which materials were human-derived versus artificial intelligence-derived. Conclusions Chatbot-derived patient educational materials have comparable readability, grade level, understandability, and accuracy to human-derived materials. Readability, grade level, and actionability may be appropriate targets for improvement across educational materials on cirrhosis. Chatbot-derived patient educational materials show promise, and further studies should assess their usefulness in clinical practice.
... Though little information is available on the risks of education through social media, it is clear that many users are now utilizing their profiles for the purpose of learning and teaching. Literature has shown that 61% of all American adults have sought health or medical information on the internet, and 49% have accessed a website that provides information about a specific medical condition or problem (Cohen & Adams, 2011). These numbers have increased due to the COVID-19 pandemic (Fullerton, 2021). ...
Article
Objective: The objective of this study was to determine the prevalence, authorship and content type of third-party reproduction-related information shared on Instagram by hashtag analysis. Methods: A list of 10 hashtags consisting of terms related to third-party reproduction was derived. Content analysis was performed in December 2021 on the most recent 100 posts for each hashtag to determine authorship and content type. Results: Our search yielded 838,151 posts. The 3 most popular hashtags were 'surrogacy', 'surrogate' and 'surrogacy journey.' Authorship of the top posts were: patients (59.2%), professional society (14.2%), for-profit commercial groups (11.4%), allied health professional (9.4%), physicians (3.3%), and other (2.5%). Patient experiences accounted for the largest share of posts (39.4%), followed by personal posts unrelated to diagnosis (21.5%), outreach posts (19.5%), advertisements (14.2%) and educational (4.8%). Patients authored the majority of posts. Conclusions: The vast majority of Instagram posts related to third-party reproduction were authored by patients who shared their own personal experiences. Within surrogacy, both gestational carriers and intended parents shared their experiences providing perspective into the surrogacy process. Physician participation may improve the quality and quantity of educational posts and offer a low-cost platform for networking and connecting with patients.
Preprint
Full-text available
Health information technology (HIT) use among foreign-born adults of Middle Eastern and North African (MENA) descent living in America is an understudied population. They are currently categorized as “White” in the United States (US) on federal forms. The purpose was to uncover the prevalence of HIT use among MENA immigrants compared to US- and foreign-born White adults before and after adjusting for other factors. The 2011–2018 National Health Interview Survey data (n = 161,613; ages 18 + years) was analyzed. HIT uses evaluated were searching for health information, filling prescriptions, scheduling appointments, and communicating with healthcare providers via email (last 12 months). Crude and multivariable logistic regression models were used to estimate the odds of each HIT use, any HIT use, and all HIT uses before and after adjustment. The most common HIT use was looking up health information, with 46.4% of foreign-born adults of MENA, 47.8% of foreign-born White, and 51.2% of US-born White adults reporting its use (p = .0079). Foreign-born adults of MENA descent had lower odds (OR = 0.64; 95%CI = 0.56–0.74) of reporting any HIT use, but no difference in reporting all HIT uses compared to US-born White adults in adjusted models. This is the first study to explore HIT use among Americans of MENA descent. Patterns of HIT use among adults of MENA descent differ from White adults. Results contribute to growing body of literature showing the health of Americans of MENA descent differs from White Americans. A separate racial/ethnic identifier is needed to better capture HIT uses among populations of MENA descent.
Preprint
Full-text available
p>Each year, Americans spend increasingly significant amounts of money on healthcare costs. The American Medical Association estimates that U.S. healthcare spending grew 2.7 percent in 2021, reaching $4.3 trillion or 18.3% of the nation’s Gross Domestic Product. Despite these statistics, healthcare quality and accessibility remain problematic. Many approaches have been suggested and implemented in response to these major shortcomings. Some of these remedies have included the proliferation of mid-tier healthcare providers such as nurse practitioners and physician’s assistants to alleviate the strain on doctors. However, there is sufficient evidence that patient education can critically impact the efficacy of the healthcare system and merits serious consideration for better patient outcomes and potentially reducing healthcare costs. This paper describes challenges and opportunities to patient education based on extensive research and practical insights from various disciplines. Furthermore, this paper sets forth recommendations for promoting a comprehensive, cohesive, and evolving patient education framework. </p
Preprint
Full-text available
p>Each year, Americans spend increasingly significant amounts of money on healthcare costs. The American Medical Association estimates that U.S. healthcare spending grew 2.7 percent in 2021, reaching $4.3 trillion or 18.3% of the nation’s Gross Domestic Product. Despite these statistics, healthcare quality and accessibility remain problematic. Many approaches have been suggested and implemented in response to these major shortcomings. Some of these remedies have included the proliferation of mid-tier healthcare providers such as nurse practitioners and physician’s assistants to alleviate the strain on doctors. However, there is sufficient evidence that patient education can critically impact the efficacy of the healthcare system and merits serious consideration for better patient outcomes and potentially reducing healthcare costs. This paper describes challenges and opportunities to patient education based on extensive research and practical insights from various disciplines. Furthermore, this paper sets forth recommendations for promoting a comprehensive, cohesive, and evolving patient education framework. </p
Article
The National Institutes of Health (NIH) recommends patient education materials reflect the average reading grade level of the US population. Due to the importance of shared decision-making in breast cancer surgery, this study evaluates the reading level of patient education materials from National Cancer Institute-designated cancer centers (NCI-DCC) compared with top Internet search results. Online materials from NCI-DCC and top Internet search results on breast cancer, staging, surgical options, and pre- and postoperative expectations were analyzed using three validated readability algorithms: Simplified Measure of Gobbledygook Readability Formula, Coleman–Liau index, and Flesch–Kincaid grade level. Mean readability was compared across source groups and information subcategories using an unpaired t-test with statistical significance set at p < 0.05. Mean readability was compared using a one-way analysis of variance. Mean readability scores from NCI-DCC and Internet groups ranged from a 9th–12th grade level, significantly above the NIH recommended reading level of 6th–7th grade. There was no significant difference between reading levels from the two sources. The discrepancy between actual and recommended reading level was most pronounced for “surgical options” at a 10th–12th grade level from both sources. Patient education materials on breast cancer from both NCI-DCC and top Internet search results were written several reading grade levels higher than the NIH recommendation. Materials should be revised to enhance patient comprehension of breast cancer surgical treatment and guide patients in this important decision-making process to ultimately improve health outcomes.
The social life of Internet users
  • S Fox
  • S Jones
Fox S, Jones S. The social life of Internet users. Washington, DC: Pew Internet & American Life Project. 2009.
Design and estimation for the National Health Interview Survey
  • S L Botman
  • T F Moore
  • C L Moriarity
  • V L Parsons
Botman SL, Moore TF, Moriarity CL, Parsons VL. Design and estimation for the National Health Interview Survey, 1995-2004. National Center for Health Statistics. Vital Health Stat 2(130). 2000.