Emergency Medicine

Predictors of patient adherence to follow-up recommendations after an ED visit

a b s t r a c t

Background: It is unclear whether factors identified during the emergency department (ED) visit predict noncom- pliance with ED recommendations.

Study objective: We sought to determine predictors of adherence to medical recommendations after an ED visit. Methods: We conducted a prospective, observational study at a single urban medical center. Eligible ED patients provided baseline demographic data as well as information regarding insurance status, whether they had a pri- mary care physician (PCP), and the impact of cost of care on their ability to follow medical recommendations. Pa- tients were contacted at least 1 week after the ED visit and answered questions regarding adherence to medical recommendations.

Results: Four hundred twenty-two patients agreed to participate in the study. At follow-up, 89.7% of patients re- ported that they had complied with recommendations made during the ED visit. Patients who were adherent to follow-up recommendations were more likely to have a primary care provider (odds ratio [OR], 2.6; 95% confi- dence interval [CI], 1.1-6.1), have an annual income of greater than $35000 (OR, 2.9; 95% CI, 1.2-7.2), and report a non-Hispanic ethnicity or race (OR, 2.8; 95% CI, 1.1-7.1). Individuals who reported that cost “sometimes” or “al- ways” impacts their ability to follow their physician’s recommendations were significantly less likely to comply with ED recommendations (OR, 2.7; 95% CI, 1.3-5.6).

Conclusion: Individuals who reported that cost affects their ability to follow their physician’s recommendations and those who did not have a PCP were less likely to follow ED recommendations. Identification of predictors of noncompliance during the ED visit may aid in ensuring compliance with ED recommendations.

(C) 2015

  1. Introduction

In the past decade, annual health care costs have nearly doubled from $9660 to $17040 for an average American family [1,2]. In 2012, more than 80 million Americans (43% of the adult population) did not see a physician or access appropriate medical care due to cost. [3] Plac- ing a high medically associated Financial burden on individuals adverse- ly impacts behavior and results in patient nonadherence [4]. In 2010, more than 20% of American families reported difficulty paying their medical bills, and 25% had their medical bills on payment plans over time [5,6]; in 2012, this increased to 30% and 26%, respectively [3]. Fur- thermore, 25% of adults reported they were unable to pay for basic ne- cessities including food, heat, or rent because of medical bills in 2012 [3]. According to the Commonwealth Fund Survey (2012), 29% of the adult population reported they had a medical problem but did not see a physician; 27% stated they did not fill a prescription; 27% said they

* Corresponding author at: Department of Family and Preventive Medicine-Division of Public Health, University of Utah School of Medicine, 375 Chipeta Way, Suite A, Salt Lake City, UT, 84108.

skipped recommended tests, treatment, or follow-up; and 20% said they did not get the specialty care they needed in the past 12 months due to cost-related barriers [3].

The literature about medical adherence is vast [7], and there are con- flicting conclusions as to which factors contribute to adherence. Demo- graphics have not been shown to be predictors of adherence, making it difficult to develop a model for predicting which patients will be nonadherent [8]. A factor that warrants further investigation is deter- mining the magnitude of impact that cost has on medical Adherence to recommendations for treatment after an emergency department (ED) visit.

Over the previous decade (2000-2010), ED visits increased by 20.2% in the United States [9]. In the state of Utah, the number of ED visits in- creased by approximately 15% and ED charges increased nearly 3-fold [10]. Although a larger percentage of individuals are receiving care in the ED, mechanisms may not be in place to ensure patients are able to obtain necessary follow-up care after an ED visit.

Given the mixed evidence regarding relevant patient characteristics that lead to nonadherence [11,12] as well as the paucity of data related to nonadherence and cost of care among ED patients, we sought to eval- uate potential predictors of medical adherence after an ED visit. Given


0735-6757/(C) 2015

Table 1

Patient demographics (n = 422)

Age, mean (SD)

43.6 (17.9)

Household size

n (%)

n (%)


87 (20.6)



126 (29.9)


242 (57.4)


80 (18.9)


180 (42.6)


47 (11.1)


0 (0)


42 (9.9)



39 (9.9)

White, non-Hispanic

375 (88.9)


1 (0.2)

African American

8 (1.9)

marital status

Asian/Pacific Islander

10 (2.4)

Never married

126 (29.9)

American Indian/Alaska Native

6 (1.4)

Living with partner

16 (3.8)


15 (3.5)


179 (42.4)


8 (1.9)


9 (2.1)



74 (17.5)


55 (13.1)


17 (4.0)


366 (86.7)


1 (0.2)



1 (0.2)

Rating of health


41 (9.7)

Some high school

39 (9.2)


90 (21.3)

High school degree

88 (20.9)


161 (38.2)

Some college

158 (37.4)

Very good

82 (19.4)

College degree

97 (23.0)


47 (11.1)

Graduate, professional

27 (6.4)


1 (0.2)


12 (2.8) Have a PCP


1 (0.2)


287 (68.2)



134 (31.8)


124 (29.3)


1 (0.2)


41 (9.7)

Currently insured


45 (10.7)


348 (82.5)


52 (12.3)


73 (17.3)


58 (13.7)


1 (0.2)


39 (9.2)

Type of insurance


54 (12.8)

Public (Medicare and Medicaid)

100 (23.7)


9 (2.1)


205 (48.6)

Level of employment


40 (9.5)

Not employed

41 (9.7)


77 (18.3)


53 (12.6)

health literacy score

Employed in home

14 (3.3)

0, third grade and below

5 (1.2)

Employed part time

48 (11.4)

1-3, fourth to sixth grade

7 (1.7)

employed full time

175 (41.5)

4-6, seventh to eight grade

57 (13.5)

Not employed but looking for work

31 (7.4)

7, High school

349 (82.7)

Not employed on disability

43 (10.2)


4 (1.0)


15 (3.6)

Urgent ED visit


2 (0.5)


300 (71.1)


120 (28.4)


2 (0.5)

previous data suggesting the impact of economic factors on medical compliance, we placed a particular emphasis on factors related to cost of care in evaluating these potential predictors.

  1. Methods

We conducted a cross-sectional survey to determine the impact cost has on medical recommendation adherence after an ED visit. The study was performed in the University of Utah ED, an urban teaching center with an annual census of approximately 40000 visits [13]. We used a convenience sample of patients presenting to the ED between 8:00 AM and midnight 7 days per week. The University of Utah Institutional Re- view Board approved the study. The study has been registered with the ClinicalTrials.gov Registration System, Identifier NCT01883778.

Trained research associates identified eligible ED patients and ob-

tained consent. Eligible patients presented to the ED over the 8-month period between January 1 and August 31, 2013. Study participants were age 18 years or older, English speaking, and were deemed psycho- logically and medically stable by the ED care provider. Patients were ex- cluded if they were prisoners, brought in by emergency medical services

(EMS) transportation due to possible severity of illness, or incapacitated by medical illness.

Participating patients were asked to complete a baseline survey in the ED and agreed to be contacted by telephone 7 to 14 days after the visit to assess adherence with recommendations made during the ED visit. A maximum of 7 attempts were made to contact each subject who consented to participate in the study.

The novel survey was constructed based upon existing literature, in- vestigators’ hypotheses, interviews with emergency physicians, inter- viewer debriefing, and FoCUS groups. The study also used modified questions from existing surveys by Allan and Innes [14], Innes et al [15], and Sehgal [16]. The survey was pilot tested in the ED with 10 pa- tients. The principal investigator (CBH) reviewed all pilot data and con- ducted an interviewer debriefing [17] with research personnel to assess the appropriateness of the survey, patients’ reactions to the questions, readability, layout and to determine face and content validity. Based upon the results of the patient pilot study, focus group, and research team feedback, the survey was revised and further pilot tested with 10 additional patients before study administration. After the second pilot test of the survey, the principal investigator (CBH) reviewed all pilot data and conducted an interviewer debriefing [17] with research

Table 2

Enrolled participants vs excluded participants

Study participants (n = 422)















not applicable


Excluded/ nonparticipants (n = 284)

P Utah














Cost Influence on Following Recommendation (P < .001)

White, non-Hispanic 88.9% 55.6% b.0002 91.8%

African American 1.9% 4.9% .02 1.3%

Asian/Pacific Islander 2.4% 4.9% .16 2.2%



American Indian/Alaska Native

1.4% 0.4% .16 1.5%


No PCP (n = 134) Has a PCP (n = 287)

Other 3.6% 18.3% b.0002 not applicable

Missing 1.9% 16.6% not applicable Hispanic

Yes 13.0% 19.0% .03 13.3%

No 86.7% 65.9% b.0002 not applicable

Missing 0.2% 15.1% not applicable

a Source, US Census Bureau: state and county quick facts. Data derived from Population Estimates, American Community Survey, Census of Population and Housing, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Econom- ic Census, Survey of Business Owners, and Building Permits http://quickfacts.census.gov/ qfd/states/49000.html.

personnel to ensure face and content validity were maintained. All data collection research personnel were trained to administer the survey. A copy of the survey has been included. (Appendix)

Not at all or Rarely Sometimes, Frequently, All the Time

Fig. 1. Patient-reported cost influence on following physician medical recommendations for those with and without a PCP.

At baseline, participants answered questions regarding demograph- ic information, their usual sources of health care, mechanism of pay- ment for health care, and reason for the present ED visit. Questions regarding the impact of cost on participants’ health care behaviors were recorded using 1 of 2 Likert scales (scale 1: 1 = no impact to 5 = greatest impact; scale 2: 1 = “not at all” to 5 = “all of the time”); these included questions about their decisions to seek care as well as

Table 3

Univariate correlates of adherence to medical recommendations


Nonadherent (n = 33)

Adherent (n = 286)

OR (95% CI)


Age (mean +- SD)


43.8 (19.6) n (%)

44.5 (18.3) n (%)

1.0 (0.98-1.02)

1.3 (0.65-2.75)




17 (51.5)

168 (58.7)

Male Race

White, non-Hispanic

16 (48.5)

29 (87.9)

118 (41.3)

252 (88.1)

0.97 (0.32-2.95)



Hispanic ethnicity Yes

4 (12.1)

8 (24.2)

34 (11.9)

30 (10.5)

0.37 (0.15-0.88)



25 (75.8)

255 (89.5)


Less than a college degree

21 (63.6)

185 (64.7)

0.95 (0.45-2.02)


College or greater Income

Less than $35000

12 (36.4)

22 (66.7)

101 (35.3)

126 (44.1)

2.5 (1.18-5.43)


$35000 or more Marital status

Partnered or married

11 (33.3)

13 (39.4)

160 (55.9)

140 (49.0)

1.5 (0.71-3.07)


Single or lives alone Have a PCP


20 (60.6)

17 (53.1)

146 (51.1)

206 (72.0)

2.3 (1.08-4.76)



Currently insured Yes

15 (46.9)

23 (69.7)

80 (28.0)

248 (86.7)

2.8 (1.25-6.42)



10 (30.3)

38 (13.3)

Type of insurance

Public (Medicare, Medicaid, Molina, and TriCare)

8 (24.2)

69 (24.1)

1.28 (0.67-2.45)


Commercial or private Health literacy score

Less than eigth grade level of literacy

25 (75.8)

4 (12.1)

217 (75.9)

39 (13.6)

0.87 (0.29-2.62)


Ninth grade or higher level of literacy Urgent ED visit


29 (87.9)

21 (65.6)

247 (86.4)

207 (72.6)

1.4 (0.64-3.01)



Rating of health Health is fair or poor

11 (34.4)

13 (39.4)

78 (27.4)

79 (27.6)

1.7 (0.81-3.58)


Health is good to excellent

20 (60.6)

207 (72.4)



Proportion of Participants










Individuals Reporting they do not Follow Their Doctors Recommendation due to Cost ( P < .01)

Nonadherent (n = 33) Adherent (n = 286)





Not at all or Rarely Sometimes, Frequently, All the Time

demographics were comparable to those of the overall Utah population (Table 2).

Most participants (68.2%) reported having a PCP, and 75.4% of pa- tients did not attempt to schedule an appointment with their PCP before presenting to the ED. Sixteen point four percent of participants stated their PCP had referred them to the ED.

Participants who completed the baseline survey were contacted by phone at least 7 days after the ED visit, and 319 (75.6%) participat- ed in this follow-up interview. There were no statistically significant demographic differences found between those completing vs those not completing the follow-up interview. Of those who completed the follow-up survey, 89.7% answered “yes” when asked if they had adhered to follow-up recommendations made during the ED visit. When asked about the specific follow-up recommendations they had received during their ED visit, 48.7% reported they followed up with their primary care provider, 12.8% followed up with a specialist,

Fig. 2. Patient-reported impact of cost on their decision to follow their physician’s recom- mendations. Question: how often do you not follow your physicians’ recommendations because of the costs?

completion of recommended care. Patients also answered questions re- lated to health literacy [18].

Reported adherence with recommendations was determined during the follow-up telephone interview. Adherence was defined as the patient’s reported compliance with the follow-up medical advice pro- vided in the ED [8]. The measures for this study were all self-reported, which has been cited as one of the more accurate records of what a pa- tient has done in the context of medical adherence [19]. Study data were collected and managed using Research Electronic Data Capture software hosted at the University of Utah [20].

We classified participants as adherent or nonadherent with rec- ommendations made during the ED visit based upon their responses during the follow-up interview to a dichotomous yes or no question: “Did you comply with the recommendations your ER doctor made when you visited the ER last week?” We then asked participants which specific recommendation they complied with after the ED visit. We compared the 2 groups using ?2, Fisher’s exact test or Wilcoxon rank sum (Mann-Whitney) for categorical variables and Student’s t test for continuous variables. Univariate and multivariate logistic regression analyses were used to examine the relationship between adherence and reported follow-up behaviors, adjusting for demographics, socioeconomic status, health insurance status, having a Primary care physician , overall health rating, and se- lected health literacy variables [8]. Partially completed surveys were included, and missing data are reported as such. All P values are for a 2-sided comparison. The resulting sample size of n = 286 patients adherent with ED recommendations and n = 33 nonadherent pro- vided 80% power using a 2-sided comparison to detect a difference of 20% and 44%, respectively, on a categorical variable and a differ- ence of 0.52 SDs on a continuous variable. Statistical analyses were performed using Stata 12.0 (StataCorp, College Station, TX).

  1. Results

Over the 8-month study period, 422 ED patients agreed to partici- pate in the study. The mean age of the study cohort was 43.6 (+-17.9) years, 57.4% were female, and 88.9% were white (non-Hispanic) (Table 1). Most participants reported being insured (82.5%), and 71.1% of the patients indicated that their ED visit was urgent or life threaten- ing. Individuals who were excluded or elected not to participate in the study were not significantly different from the study population by sex but differed in their reported race and ethnicity. Participant

3.3% received additional testing, 15.4% filled recommended medica- tions, 5.2% were admitted to the hospital, and 7.8% indicated “other” as their response, which included “rest,” “taking time off work,” and “drinking plenty of fluids.”

We conducted univariate regression analysis to evaluate the as- sociation of demographic covariates with reported adherence to ED follow-up recommendations. In the univariate logistic model, 4 co- variates were significantly associated with adherence to ED recommendations: income, Hispanic ethnicity, having a PCP, and in- surance status (Table 3).

We created a multivariate model that included age and education, which have been reported to be associated with adherence in previous studies as well as the variables which were statistically significant in our univariate analyses, with the exception of insurance status due to its col- linear association with having a PCP (odds ratio [OR], 5.9; 95% confi- dence interval [CI], 3.3-10.6; P b .001). Having a PCP was independently associated with adherence to ED follow-up recommen- dations (OR, 2.6; 95% CI, 1.1-6.1; P = .03). In addition, those with a self-reported annual income of greater than $35000 had an OR for com- pliance of 3.0 (95% CI, 1.2-7.2; P = .02) compared to those with an in- come of b$35000, and those who did not report Hispanic ethnicity had an OR of 2.8 (95% CI, 1.1-7.1; P = .03) compared to those reporting Hispanic ethnicity. No other covariates were significantly associated with adherence in the multivariate model.

In addition, we noted a significant difference between individuals with and without a PCP and the impact of cost on compliance with medical recommendations both at baseline and follow-up. At base- line, 40.3% of those without a PCP indicated that cost at least some- times impacted their intent to follow up with medical recommendations vs 22.9% of those with a PCP (P b .001). We noted a similar pattern at the follow-up survey (36.8% vs 17.5%; P = .02) (Fig. 1).

Finally, individuals were asked how often they do not follow their physician’s recommendations due to cost. Those who reported they are rarely or never nonadherent to their physician’s recommendations due to cost were significantly more likely to report compliance with ED follow-up recommendations than those who reported that cost sometimes or always has an impact on their adherence (OR, 2.7; 95% CI, 1.3-5.6; P = .01) (Fig. 2).

  1. Discussion

There are several limitations to our study. First, the data were self-reported, and, therefore, the reliability of the data is dependent upon the integrity and completeness of individual responses [21]. In addition, the quality of the responses relies on subjects’ under- standing of the survey items. Our results may not be generalizable to other Geographic regions or populations but may be unique to the specific ED population we studied [14]. Because of the cross-

sectional study design, there may be alternative explanations for the study results [22].

The definition of adherence can be widely interpreted, and, to date, there is not a criterion standard for measuring compliance and adher- ence to treatment recommendations [23]. In our study, patients were asked if they completed the recommendations made at their ED visit without further definition of adherence provided to them for clarifica- tion. In addition, we asked patients if they adhered with the recommen- dations made at the ED visit and specifically which recommendations they followed. However, the study design did not allow for query of the electronic medical record to verify the patient discharge instruc- tions, specific disease processes, or disease acuity. Similarly, our study is limited given that we did not review ED discharge instructions to de- termine the nature of the recommendation provided in the ED and com- pliance rates with various types of recommendations. Compliance with an antibiotic prescription would be more significant than a recommen- dation to drink plenty of fluids, but determination of compliance rates with specific types of discharge instructions was beyond the scope of our study.

The inclusion of only English-speaking individuals, recruitment of participants from 8 AM to midnight, and inclusion of both admitted and discharged patients may introduce potential selection biases and have an impact on the generalizability of the study results. Further- more, exclusion of individuals who arrived by EMS may have altered study results because reason for EMS transport was not determined. We specifically excluded this population due to concerns about en- rollment of patients of high acuity and interference with their care during the enrollment process. However, in doing so, we may have excluded patients of lower acuity who may have used EMS as their primary means of transport to the ED. Moreover, we did not attempt to include a scale measuring the threshold at which cost becomes an issue. If the perceived cost was high, reported adherence may decline [24,25].

Somewhat surprisingly, we noted a high level of self-reported compliance with recommendations made during the ED visit, with the large majority of patients reporting compliance with these rec- ommendations during the week after their ED visit. Our results illus- trate the challenge of identifying reliable socioeconomic and demographic factors, which predict potential nonadherence [26]. In addition, they demonstrate the challenges of measuring compliance in a health care setting [27].

Perhaps the most significant and reliable predictor of compliance in our study was a patient’s report of having a PCP. In most cases, ED recommendations included follow-up with a health care provider. Those who reported having a PCP were much more likely to adhere to ED recommendations, and the fact that nearly half of those who complied with recommendations did so through PCP follow-up ex- plains, in part, the significance of having a PCP in assuring follow- up with ED guidance.

Also of particular note was the significance of the question: “How often do you not follow your physician’s recommendations because of costs?” When asked this question during the ED visit, those who report- ed that cost rarely or never impacts their follow-up on recommenda- tions were also those who were more likely to follow up on the ED recommendations.

Prior studies have identified similar findings to ours in the relation- ship between insurance status, having a PCP, and follow-up on ED rec- ommendations. Asplin et al [28] found that being uninsured vs having private insurance or Medicaid was associated with a person’s ability to obtain an appointment for follow-up care after an ED visit. A random- ized trial by Kyriacou et al [29] found that having a PCP increased ED pa- tients’ follow-up compliance.

The relationship between Hispanic ethnicity and noncompliance was unclear. We excluded patients who were non-English speaking, so we would not conclude that significant language barriers prevented understanding of discharge recommendations impacted this finding.

Further study of this relationship may help in identifying barriers to fol- low up if future investigations confirm a similar independent associa- tion between ethnic and racial identity and ED noncompliance. In addition, it may be necessary to evaluate language discordance for indi- viduals who speak English as a second language to determine if there is an association between nonadherence and language complexity in dis- charge instructions.

In approaching the issue of insurance status and follow-up compliance, emergency physicians may perceive that they cannot discuss a patient’s insurance status or ability to pay for future care due to a possible conflict with Emergency Treatment and Labor Act. The findings in our study may offer alternative approaches to inquire regarding factors which may predict a patient’s ability to comply with ED recommendations. As noted, we found a collin- ear relationship between insurance status and having a PCP and thus did not include insurance status in our multivariate analysis model. A patient’s negative response to the question, “Do you have a PCP?” would suggest that the individual is less likely to follow up on the ED recommendations, and additional queries may be necessary to determine what may be done to help facili- tate compliance with the ED recommendations. In addition, a physician may ask, “How often does cost affect your ability to fol- low up on your physician’s recommendations?” Although this question becomes a much more direct inquiry into a patient’s ability to pay for their care and for the follow-up recommenda- tions, a patient’s response to this question may also help guide the physician toward a more detailed discussion of measures to assist in compliance with recommendations.

Our findings indicate that discussions about financial barriers to adherence are warranted in the ED. Effective interventions such as sharing information about low-cost pharmacies, using generic medications, and facilitating the scheduling of follow-up appoint- ments at discharge from the ED may increase medical adherence, particularly among individuals for whom barriers to compliance are identified. Simple questions related to the patient’s PCP and the impact of cost on adherence may assist in identifying the need for this additional assistance.

  1. Conclusion

Emergency department patients who did not have a PCP, who re- ported an income less than $35000 per year, or who self-identified as Hispanic were less likely to follow up on recommendations given in the ED. In addition, those who acknowledged during the ED visit that cost of care is a barrier to follow up on their physician’s recom- mendations were less likely to comply with ED recommendations. Emergency physicians may wish to use tools to identify those at greater risk for noncompliance and provide additional resources to these individuals to aid in compliance.

Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ajem.2015.07.032.


We thank Matthew Broadwater-Hollifield, MD, and Krista Viau for their thoughtful review of an earlier version of this manuscript. Study data were collected and managed using Research Electronic Data Capture electronic data capture tools hosted at the University of Utah [20]. Research Electronic Data Capture is a secure, Web- based application designed to support data capture for research studies, providing (1) an intuitive interface for validated data entry, (2) audit trails for tracking data manipulation and export pro- cedures, (3) automated export procedures for seamless data down- loads to common statistical packages, and (4) procedures for importing data from external sources. Research Electronic Data Cap- ture is supported by 8UL1TR000105 (formerly UL1RR025764)

National Center for Advancing Translational Sciences/National Institutes of Health at the University of Utah, College of Nursing.


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