a b s t r a c t

Background: Emergency Department (ED) providers’ disposition decision impacts patient care and safety. The ob- jective of this brief report is to gain a better understanding of ED providers’ disposition decision and risk tolerance of associated outcomes.

Methods: We synthesized Qualitative and quantitative methods including decision mapping, survey research, sta- tistical analysis, and word clouds. Between July 2017 and August 2017, a 10-item survey was developed and con- ducted at the study hospital. Descriptive and statistical analyses were used to assess the relationship between the participant characteristics (age, gender, years of experience in the ED, and level of expertise) and risk tolerance of outcomes (72-h return and negative outcome) associated with disposition decision. Word clouds facilitated pri- oritization of qualitative responses regarding information impacting and supporting the disposition decision. Results: Total of 46 participants completed the survey. The mean age was 39.5 (standard deviation (SD) 10 years), and mean years of experience was 9.6 years (SD 8.7 years). Decision map highlighted the connections between patient-, provider-, and system-related factors. survey results showed that negative outcome resulted in less risk tolerance compared to 72-h return. Chi-square tests did not provide sufficient evidence to indicate that the re- sponses are independent of participants characteristics – except age and the risk of 72-h return (p = 0.046). Conclusion: Discharge decision making in the ED is complex as it involves interconnected patient, provider, and system factors. Synthesizing qualitative and quantitative methods promise enhanced understanding of how pro- viders arrive to disposition decision, as well as safety and quality of care in the ED.

(C) 2017

Introduction

Disposition decision in the Emergency Department (ED) is a critical decision that determines the level of care that an individual requires after leaving the ED – including admission to the hospital or discharge to home. Disposition decision has been shown to impact patient care and safety [1]. Delayed or incorrect disposition can cause transfer to a suboptimal level of care, and potential return to the health system [2-4]. While the impact of disposition decision in the ED has been stud- ied [5,6], there is a gap in the literature on how ED providers i) make the disposition decision [1], and ii) perceive potential Negative outcomes associated with disposition decision.

Disposition decision in the ED is a complex clinical decision process that includes synthesis of multiple Information sources. Studies have shown that provider’s disposition decision is impacted by their risk

* Corresponding author.

E-mail addresses: Muge.Capan@ChristianaCare.org (M. Capan), pigeon@udel.edu

(J. Pigeon), DMarco@Christianacare.org (D. Marco), JPowell@Christianacare.org (J. Powell), KGroner@ChristianaCare.org (K. Groner).

tolerance [7-9]. Difficulty of quantifying the risk associated with nega- tive outcomes can result in higher rate of hospital admissions [10]. Syn- thesizing qualitative and quantitative methods in health care have been shown to address multiple facets of the health services research more comprehensively [11] and reduce methods-induced bias [12]. While previous studies used qualitative methods, e.g. FoCUS groups, to study Disposition decisions in the ED [1,13], and mixed methods to study out-of-hospital trauma triage decisions regarding hospital selection

[14] there is an emergent need to utilize qualitative and quantitative methods to map the components of and risk tolerance associated with disposition decision. The objective of this brief report is to gain a better understanding of ED providers’ disposition decision and risk tolerance of associated outcomes by synthesizing qualitative and quantitative methods.

Methods

We used qualitative (i.e. decision mapping, survey research, word clouds) and quantitative methods (statistical analysis). Decision map- ping is a technique that allows the decision-making processes to be

https://doi.org/10.1016/j.ajem.2017.11.018

0735-6757/(C) 2017

analyzed by decomposing the decision process into comprehensible components [15]. Survey research refers to “collecting information from a selected population using standardized instruments with the goal of making some inference about the wider population” [16]. A word cloud is “a visual depiction of words in which the more frequently used words are effectively highlighted” [17]. Finally, statistical analysis was used to assess the relationship between the survey participant characteristics and their risk tolerance regarding outcomes following disposition decision.

Study setting and population

Between July 2017 and August 2017, we conducted a 10-question survey developed in Research Electronic Data Capture (REDCap), a se- cure, web-based application [18] distributed to ED providers at the study hospital, a not-for-profit healthcare system with over 53,000 hos- pital admissions per year and 1100 Hospital beds. The study hospital ED is a full-service emergency department and a Level 1 Trauma Center. We e-mailed survey invitations to ED providers who made the disposition decision. Nurses, technicians, and other roles in the ED were excluded since they do not make disposition decisions.

Data collection

Survey data was collected anonymously using a REDCap form in- cluding four questions regarding the participant characteristics (age, gender, years of experience in the ED, and level of expertise defined as attending, resident, or physician assistants), and six questions about dis- position decision specific to discharging an ED patient home (Table 1). Expert determination of content validity was utilized to ensure that the survey items shown in Table 1 can be interpreted as meaningful measures of risk perception associated with the discharge decision in the ED and potential associated outcomes using clinical scenarios. The questions 1-4 represent different decision scenarios and questions 5- 6 are open-ended questions. The first question represents the case of a patient with risk of 72-h return to the ED. The next question is the deci- sion scenario with risk of negative outcome instead of return. Risk toler- ance of participants are measured in questions 1-4 using multiple choice answers ranging from 0 to 100 in increments of 10 (e.g. 0-10%, 10-20%, etc.). To capture the risk tolerance associated with discharging a patient home, questions 5 and 6 are designed as open-answer ques- tions focusing on information impacting and helping the disposition

decision.

Table 1

Survey questions regarding the disposition decision in the Emergency Department (ED)

Results

Characteristics of study subjects

Over the study period, we had 46 participants. The mean age was 39.5 (standard deviation (SD) 10 years), and mean years of ED experience was 9.6 years (SD 8.7 years). 65.2% of participants were male. As for level of expertise, 60.9% of the respondents were attending, 28.2% were residents, and 10.9% were physician assistants.

Decision mapping

Several study hospital ED clinicians were interviewed with ad hoc methodology to capture the components of disposition decision in the ED. Fig. 1 illustrates the final decision map.

The disposition decision process starts when a patient arrives to the

ED. Each light grey box is a data element that encompasses the data points represented by the white boxes (Fig. 1). These elements provide the information to make the key decisions. We note that Fig. 1 does not represent a decision tree analysis, but illustrates a framework highlight- ing the interconnected nature of key decision elements based on obser- vations and clinical feedback including patient-related (e.g. patient severity), provider-related (e.g. risk tolerance), and system-related fac- tors (e.g. available staffing).

Survey research results

A descriptive analysis quantified the frequency of responses to questions 1-4 in each possible range representing the risk tolerance (Fig. 2).

For the first question with regards to 72-h return (Q1 in Fig. 2), the majority of participants chose the 10-20% range followed by the 0- 10% range. No participants felt comfortable with risk of 72-h return over 40%. When the decision scenario changed to a negative outcome instead of return (Q2 in Fig. 2), over 90% of participants chose the 0- 10% range. Having insufficient resources at home (Q3 in Fig. 2) lead par- ticipants to select lower risk ranges. Around 75% of providers chose the 0-10% range, and 20% chose the 10-20% range. No participants were comfortable with a risk over 30%. As for patients with no primary care (Q4 in Fig. 2), about 50% the participants chose the 0-10% range and 40% chose the 10-20% range.

Statistical analysis

Chi-square tests were used to test whether the responses are inde- pendent of the collected participant characteristics (i.e. age, gender, years of experience in the ED, and level of expertise). To categorize

the continuous characteristics age was divided into two categories

Question number

Question text

(b 40 and >= 40), and years of ED experience into two categories (0- 5 years and N 5 years) where the thresholds were selected to create bal-

Imagine that you are assessing a patient in the ED who is not a trauma/stroke/sepsis patient. You are asked to decide to either send the patient home or admit them to the hospital. At what percent chance of return to the ED in 72 h would you be comfortable sending the patient home?

Imagine that you are assessing a patient in the ED who is not a trauma/stroke/sepsis patient. You are asked to decide either to send the patient home or admit them to the hospital. At what percent chance of negative outcome would you be comfortable sending the patient home?

How would you respond to question 1 if everything remains the same except the patient does not have sufficient resources at home to care for their illness?

How would you respond to question 1 if everything remains the same except the patient does not have access to a primary care doctor?

What information typically impacts your disposition decision?

What would help you to improve your decision process for disposition?

anced samples. Chi-square tests did not provide sufficient evidence to reject the null hypothesis at the ?=.05 level that the responses are in- dependent of the characteristics, except age and risk tolerance regard- ing 72-h return (p = 0.046). Further analysis revealed that participants younger than 40 spread their answers out between 0 and 10, 10-20, and 20-30 ranges, and those older than 40 had two thirds of their answers in the 10-20 range.

Word clouds

We utilized word clouds to prioritize responses to questions 5-6 (Fig. 3). The responses were categorized into common themes by searching for key words in following categories: clinical aspects (e.g. pa- tient severity), social aspects (e.g. social work presence), follow up (e.g. reliability of follow up), administrative aspects (e.g. less administrative

Fig. 1. Decision map of disposition decision in the ED including decision points (dark grey diamonds), decision elements (light grey boxes), data points (white boxes) and final decision points (black circles). ESI stands for Emergency Severity Index [19].

help), and efficiency of clinical practices (e.g. more rapid turn-around on labs). If key words from multiple categories were found, the response was counted multiple times.

For the question about information impacting disposition decision the most frequent categories were follow up, social aspects, and clinical aspects. For the question about factors improving disposition decision,

Fig. 2. Distribution of responses to survey questions 1-4 including 72-h return (question 1 referred as Q1), negative outcome (question 2 referred as Q1), insufficient resources at home (question 3 referred as Q3) and no primary care doctor (question 4 referred as Q4).

Fig. 3. Word clouds for the question “What information typically impacts your disposition decision?” on the top and “What would help you to improve your decision process for disposition?” on the bottom.

follow up had the highest frequency, followed by social aspects and ef-

ficiency of clinical practices.

Discussion

Discharge decision in the ED is complex as it involves interconnected patient, provider, and system factors. The ED providers make decisions with limited information, uncontrollED patient volume, and varying clinical acuity [20]. Studies have indicated that ED providers experience concerns about health consequences for their patients after discharging home [1]. Understanding the factors contributing to discharge decision and risk tolerance for associated consequences is fundamental to im- proving care quality and safety. While the importance of the ED dispo- sition as a clinical decision making process has been studied, there is a need for further investigation of data-driven approaches and novel visu- alization methods to better understand the complex nature of discharge decisions in the ED. In this brief report, we provided a framework to in- tegrate qualitative and quantitative methods to better understand of ED providers’ disposition decision and risk tolerance of associated out- comes, e.g. 72-h return and negative outcome.

While the current study is limited by the small sample size of partic- ipants, single study hospital, and survey questions that do not target any specific type of medical condition, our findings indicate the potential benefit of further exploration of the relationship between provider characteristics, disposition decision process, and perception of potential consequences. Specifically, survey results showed that risk of negative outcome prompted providers to be less tolerant to higher risk level

compared to 72-h return. Lack of patient resources at home caused risk tolerance to be lower than lack of access to primary care, and indi- cated that patient resources makes a greater impact on the disposition decision. Another limitation is that the findings are derived from a single health system, and may not be generalizable to other health care systems.

The decision map highlighted the connections between patient-, provider-, and system-related factors contributing to the disposition de- cision. Chi-square tests did not provide sufficient evidence to reject the hypothesis that the responses are independent of the participants char- acteristics, except age and the question regarding the disposition deci- sion with risk of 72-h returns (p = 0.046). Survey results indicated that providers with age b 40 had more variation in risk perception re- garding the risk of 72-h returns compared with participants >= 40. Future studies with larger sample size will further evaluate the relationship be- tween participant characteristics and response categories. While the survey was used as an initial tool to gather feedback from the ED pro- viders, future studies can complement this approach with additional qualitative methods, such as focus groups. Word clouds showed that participants endorsed follow-ups as the most important factor to pro- vider decision making, and that more follow-up and social information (e.g. (family and social determinant of health)) would help ED providers to make better decisions which should be explored further in the future studies.

Acknowledgements

We acknowledge the study hospital Emergency Department provid- er team for their assistance.

Funding

This research did not receive any specific grant from funding agen- cies in the public, commercial, or not-for-profit sectors.

Conflicts of interest

None

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