Challenges in inpatient care coordinators’ clinical workflow and opportunities in designing a health IT solution: A mixed methods study

Abstract

Introduction

Inpatient care coordinators (ICCs) in the United States play a critical role in case management and care transition. ICCs spend a large amount of time in chart review and documentation through electronic health record (EHR) systems. However, significant knowledge gaps exist regarding their workflow barriers and their use of health information technology (Health IT). Using only quantitative or quantitative methods does not provide a comprehensive picture about ICC’s workflow due to its complex and dynamic nature. This work aimed to address this gap by conducting a mixed-methods study to understand the workflow of ICCs and identifying challenges in care deliver and documentation activities.

Methods

The study adopted a concurrent triangulation design including qualitative interviews with 12 ICC staff members in the United States followed by extraction of their EHR event logs for one month. The qualitative interview data were analyzed thematically, and the log data were analyzed statistically. The results were triangulated and interpreted.

Results

Three major workflow barriers faced by ICCs were identified: long travel time, heavy documentation load, and suboptimal communication. The event logs provided empirical evidence to support the workflow barriers identified in the interviews, especially in travel time and documentation load.

Discussion

ICC workflow has several inefficiencies. The study generated four design considerations to develop a Health IT solution: Mobility, EHR integration, Team-based Communication, and User Adoption to improve workflow efficiency and care coordination. Using a mixed-methods approach is effective and efficient in collecting and analyzing clinical workflow.

Keywords

Social workers workflow efficiency electronic health records

Introduction

Inpatient care coordinators (ICCs) working in hospitals in the United States help patients, their families, and caregivers with psychosocial, financial, and emotional needs throughout their hospitalized care process. Many of these needs are related to patients’ social determinants of health, or factors that affect a wide range of health risks and outcomes. ICCs address patient needs in a variety of ways, including through screening, referrals, and care coordination.¹ Care coordination for individuals with chronic diseases relies on communication across multiple health care settings to organize care, particularly during care transitions, to prevent unnecessary hospital readmissions.^2,3 Coordinating transitions between hospitals requires the use of timely alerts and documentation in the electronic health record (EHR). However, ICCs are often tasked with unnecessary communication and documentation duties during this process, leading to their high job turnover and low job satisfaction rates.^4,5

Several studies have been conducted to improve care coordination. Watson et al. developed a tool to measure inpatient care coordination and identified teamwork, handoff, and transition as three consistent and significant factors.⁶ Doessing and Burau highlighted the case and care complexity in their systematic review on care coordination of multimorbidity patients.⁷ Khera et al. extended the stakeholders of care coordination to include payer representatives, patient advocates, and community-based organizations in the hematopoietic cell transplantation setting.⁸ Hsiao et al. recognized the importance of these factors and developed a comprehensive program to improve care coordination in an urban academic hospital in the United States.⁹ Well-coordinated care also has the potential to reduce the utilization of an emergency department, improve patient outcomes, and eliminate health disparities.^10–12 Recently, health information technology (Health IT) especially electronic health records (EHR) have been utilized to support care coordination and delivery. Although Heath IT is powerful and necessary to maintain information availability and continuity in care coordination, several issues remain including accessibility and communication and need to be resolved by a multidisciplinary care team.^13,14

Unfortunately, there is a lack of research focused on ICCs and how they use Health IT. Specifically, there is very little work on potential negative effects to ICC workflow and workload caused by poor Health IT adoption. A poorly designed Health IT solution can result in negative consequences and even patient harm.^15,16 In fact, current clinical documentation supported by computer systems frequently impede, rather than support, clinical workflow.^17,18 On the other hand, a well-designed Health IT solution can support ICCs and improve the collection and use of patient data in the care continuum, streamline work processes, increase ICCs’ job satisfaction, and further improve patient outcomes as a result of the patients’ needs being properly addressed.¹⁹ The closest seminal article found examined the flow of information in chronic disease care, which was utilized to develop guidelines for the design of a Health IT solution to support chronic disease care.²⁰ A research gap remains in using Health IT to support ICC workflow and help address patients’ needs broadly in education, economic stability, and social and community context.^21,22 Therefore, understanding ICC’s workflow and providing the context of use would be a critical first step to design a Health IT solution with high usability to improve inpatient care coordination and delivery.

Most of the previous studies in this area used qualitative research methods;^23,24 very few (or none) of them used informatics-based methods to gain a deeper understanding of care coordination. Clinical workflow analysis is a major research topic in the field of medical informatics and has generated several frameworks and methodologies^25–28 which can be directly applied to quality improvement and health service research in the care coordination setting. Moreover, using mixed methods can provide more nuanced descriptions and empirical evidence about barriers faced by ICCs. Another benefit of using a mixed-methods design is to reduce either the physical or the online contact time with participants²⁹ during the COVID-19 pandemic because the data collection does not solely rely on interviews and observations. Altogether this study aimed to bridge the knowledge gap between care coordination and medical informatics and conduct a mixed-methods study to analyze the workflow of ICCs.

In this study, the objectives are two-fold: 1) to understand ICC’s workflow and identify barriers, especially in their documentation activities and 2) to demonstrate the effectiveness and efficiency of using a mixed-methods approach to analyze clinical workflow as an alternative to the traditional qualitative or time and motion studies. The research questions of the current study include: 1) what is a typical day of an inpatient care coordinator? 2) what challenges exist in terms of their care delivery and documentation activities? and 3) what patterns in the EHR event logs reinforce or supplement our findings from the qualitative interviews?

Methods

Clinical setting

This study was conducted with the Department of Social Work at the University of Cincinnati Medical Center (UCMC), a leading academic medical center with more than 700 beds and a new EHR system provided by the Epic System Corporation in 2015. The Department of Social Work at UCMC consists of four units: inpatient, outpatient, emergency, and psychiatry. The department has a total of 50 ICCs who are formally trained in either nursing or social work. These ICCs perform the same tasks regardless of education or training.

Study design

In order to understand the environment and context,³⁰ for example, understand existing workflow patterns and barriers,³¹ Time and motion studies (TMS) have been regarded as a “gold standard” and are widely used in the healthcare industry to analyze clinical workflow.²⁷ A mixed-methods approach including cognitive ethnography and TMS has been proposed to measure the impact of delivery-centric interventions on clinical workflow.³² However, TMS has limitations and can suffer from methodological inconsistencies.³³ Conducting a TMS can be costly, especially in terms of resources and scheduling. Recent advances in workflow analysis with the secondary use of EHR data provide opportunities to automatically monitor and optimize clinical workflow,^34–37 as well as provide a more cost-effective and scalable solution for clinical workflow analysis.³⁸ Building on top of this methodological breakthrough, a concurrent triangulation design using qualitative interviews and EHR event logs was adopted to analyze the clinical workflow of ICCs. Additionally, using a concurrent triangulation design³⁹ can improve the efficiency of clinical workflow analysis. This study was reviewed and approved by the UC Institutional Review Board (IRB# 2017-7149).

Qualitative interviews

Participant recruitment

From the institution’s ICCs, we recruited managers and staff using convenience sampling. Participants were chosen based on the managers’ recommendation after considering their years of experience and availability for interview. Each of the participants sat for a 30-min, semistructured interview. The interview questions were designed in the following areas: 1) job title and responsibility, 2) daily routines and processes, 3) communication with other team members, 4) documentation responsibility and barriers, and 5) suggestions to improve workflow efficiency. The interviews were recorded and transcribed verbatim by one of the research team members. After transcribing, each transcription was reviewed to ensure accuracy and completeness. Any omissions or deviations from the audio recording were fixed. The privacy of each participant was kept by de-identifying them and referring to them as “P01,” “P02” … in all interviews. No specific patient information was collected.

Interview data analysis

Interview data were analyzed in two phases. In the first phase, the interview transcriptions were coded to construct a workflow chart. Using the Work Elements Model,⁴⁰ Actors, Actions, and Artifacts were coded, and a swim-lane diagram was drawn (Figure 1). The lanes represent the actors and the description of the rectangles represents the actions (verb) and the artifacts (noun). The starting and ending points and the decisions are presented in the shapes of ovals and diamonds, respectively.

Figure 1.

Workflow of inpatient care coordinators. Boxes in the physician and patient lane represent the actions that care coordinators completed with the roles, not the actions completed by the roles. Of note, Office Activities may include phone calls from physician and other team members as a source of interruption.

In the second phase, the barriers that prevented ICCs from completing workflow tasks were summarized using a thematic analysis.⁴¹ To accomplish this, the interview transcriptions were reviewed and a coding scheme was developed inductively to map the task-related and common workflow barriers, as shown by the numbered circles in Figure 1. The researchers met twice to develop a comprehensive picture of ICCs’ daily workflow, tasks, and “barriers” (e.g. moments throughout the day where workflow barriers were identified among the ten interviewed staff ICCs). Finally, the barriers were mapped to the consolidated workflow chart using numbered circles. The workflow chart and barriers were created primarily using staff data (P03–P12). The workflow chart is a general workflow summary and does not show individual differences between ICCs that may exist

EHR event logs

Log data extraction

All study participants’ EHR event logs in January 2018 were extracted from UC Health’s electronic health record (EHR) system through the UC Center for Health Informatics. This dataset contained 215,206 records, including information such as user identifier (WHO), timestamp (WHEN), vendor-defined activity types (WHAT), and workstation name (WHERE). The data were properly de-identified based on existing data transfer protocols and securely stored and analyzed in an internal server at the UC College of Medicine. Again, the data from the managers (P01 and P02) were excluded from the analysis due to their special role and limited number.

Log analysis

The EHR logs were analyzed with these three focuses: daily volume, task, and location. First, a line chart was created to represent the daily volume of EHR interactions. Daily volume is an approximate measure for EHR use per day. This is defined as the total number of logs of the non-manager participants (N = 10) by the hour of the day. Since ICCs perform different access types (e.g. Read or Modify) while using the EHR, separate lines were used in a line chart to represent different access types. The line chart only included workday data (Monday through Friday). Second, vendor-defined tasks in the EHR logs were counted, and top tasks were listed by access types. This was approximated by the data stored in the METRIC_GROUP column of the ACCESS_LOG table in the Clarity database of the Epic EHR system. This column recorded where the event happened in the EHR (e.g., Patient Clinical Info or Clinical Notes). Our analysis on event areas versus types was not applied to event locations since the actual location of a workstation was not mapped to the floor plan.

Triangulation and interpretation

The qualitative and quantitative results were compared and contrasted to provide a more comprehensive understanding about the ICC’s workflow and bottlenecks. Additional analysis was done to demonstrate the potential differences between two segments in the workflow based on both types of results. For example, the qualitative results may find that the participants switched tasks more often in one period versus the other. In this scenario, the tasks are denoted as A, B, and C, and a sequence of tasks is generated (e.g., A -> B -> A -> C -> C). Then, the task switches (A to B, B to A, and A to C) are identified and the ratio is calculated by dividing the total number of actual switches by the total number of possible switches. In this example, the sequence has five tasks with three switches; the total possible switches are four. Therefore, for this participant, the “task change” ratio is three over four (or 0.75). Of note, the ratio is used rather than the frequency of changes because each participant may generate sequences of different lengths. Using this calculation, the ratios of sequences in two time periods are calculated; their means or medians are compared in two groups using either a one-way ANOVA or a Kruskal–Wallis test, depending on the normality of the distributions. The same analysis can be applied to the location switches if it is significant as indicated by the qualitative results.

Results

Participant characteristics

We interviewed two ICC managers and 10 staff members. All the participants were female. Most of the participants received their degrees in nursing, with only two inpatient managers and two care coordinators receiving a degree in social work. Most participants saw between 10 and 15 patients a day. The staff members (excluding the two managers) have varying years of services at their current positions, with an average at 3.5 years. Most participants stayed in their current position for less than five years, which is consistent with current literature suggesting a high ICC turnover rate.⁵ Table 1 summarizes the participants’ demographics.

Table 1.

Description of the characteristics of the participating ICCs (n = 12).

Characteristics		Value, N (%)
Sex
	Female	12 (100.00)
Clinical role
	Manager	2 (16.67)
	Staff	10 (83.33)
Education
	Master of Social Work	3 (25.00)
	Registered Nurse	8 (66.67)
	Both	1 (8.33)
Time in current position
	<1 year	4 (33.33)
	≥1 year and <2 years	2 (16.67)
	≥2 years and <5 years	3 (25.00)
	≥5 years	3 (25.00)

Workflow diagram

The qualitative analysis of semistructured interviews generated a workflow chart (Figure 1) that showed a typical day for ICC staff members. The data from the two managers were excluded in this analysis because their workflow patterns were distinct due to their roles. As Figure 1 shows, most of the coordinators arrived between 7:00 and 7:30 am and began their day by reviewing patient lists and charts. Chart review consists of various activities and includes the review of patient notes to determine destination after discharge. The ICCs also engaged in several office activities in the morning. These included making appointments for patients, receiving, or making, phone calls, arranging transportation for patients, and sending referrals. At 10 am, the coordinators met with physicians to round on their patient lists. This task, “Rounding” (see Figure 1), creates a significant workflow barrier. If Rounding is delayed, later tasks are delayed and more work needs to be done.

After Rounding, ICCs spend the rest of the day (11 am–5 pm) in a cycled pattern of activities at both the patient bedside (assessments and documentation using paper forms) and in their office (continuing office activities and documenting on patients on EHR). For example, after prioritizing patients (e.g. moving patients on the list to form a working queue), ICCs have the option to complete assessments on patients, catch up on various office activities, or catch up on documentation in their office. If they chose to complete assessments, they may visit multiple patients prior to returning to their offices. Some of the care coordinators also document outside of their offices while in between seeing patients.

Workflow barriers

The interview data analysis yielded three thematic barriers that prevent ICCs from completing their daily tasks. These themes include: 1) long travel time, 2) heavy and unorganized documentation load, and 3) suboptimal communication and use of time (Table 2).

Table 2.

Themes of workflow barriers.

	Subtheme	Number of participants mentioned^a	Representative quotes from participants
Long Travel Time	Excessive traveling between locations	7	“My office is on floor 7, it’s more convenient for me. A lot of my other coworkers have an office on 3, so they are constantly go up and down” (P05)
Long Travel Time	Issues with accessing technology in any given location	5	“Either [the computer] is being used by a nurse or its not working, so I would say it’s a 50/50 shot if I get to use the computer… whether its not working or the nurse is using it.” (P08)
Heavy and Unorganized Documentation	Bad high-risk screen	7	“The “high risk screen” is a waste of time, I really feel that it is. I think that it doesn’t answer the questions we need it to like is the patient active with home care? It’s just not effective enough. Basically, I only use the screen because it is mandatory for every patient in the whole hospital.” (P03)
	Lack of real time charting	6	“Finding out how to document more in real-time with still giving the patients time with you because a lot of my time is sitting in my office trying to type things up from my time spent with the patients, and that cuts into the time that I have available to go talk with other patients.” (P05)
	No progress note template	4	“I think templates are always good just for continuity, because everyone in our department does it differently.” (P09)
	Too many data entry systems	2	“Doing that and then having two different systems, so having EPIC and E-send. Having to put stuff in EPIC and then having to go back and put it in e-send…There’s overlap and no it doesn’t share between systems. Like in EPIC, if I do replacement, it would be helpful for whenever I put a placement in EPIC, for it to go directly into e-send. Some things overlap, like the patient information stuff, it’s all in there…” (P10)
Suboptimal Communication and Use of Time	Not easy to talk with doctors and/or medical staff	4	“So, I think the biggest holdup is kind of just waiting around for our doctors to get their paperwork in so we can get people set up” (P08)
Suboptimal Communication and Use of Time	Not enough clarity in documentation notes	2	“I think just having a lot to do, not having the time to do it. I mean today is crazy hectic, and there’s a lot of patients, all with a lot of needs, yesterday was a holiday, it’s all backed up, and trying to figure out what other people documented, reading other people’s notes” (P09)

Themes were summarized from the 10 care coordinators, which excludes the two managers.

Travel was an issue due to the locations of ICCs’ offices, which are assigned based on room availability. Many ICCs do not have offices near their patients. ICCs’ patient rooms can also be in multiple buildings due to the lack of available rooms. In the case of participant “P03,” her office was in an extremely inconvenient location:

“So, I’m on [floor] three, and all of my patients are on [floor] seven. I’m on the baby floor, and we lockdown a lot. So, if it’s locked down, the elevators don’t work and you can’t do stair well, so you’re kind of stuck on the floor… If they are going to transport a baby from say, the NICU, to a regular room, they shut the whole floor down. And this happens multiple times a day… I can’t get anywhere.” – P03

Second, heavy and unhelpful documentation requirements were an issue for ICCs. A mandatory “high-risk screening” is required to be filled out for all patients in the EHR. The high-risk screening tool in the EHR is a flow sheet that was originally designed as a tool to determine whether a patient requires attention from ICCs. Unfortunately, the high-risk screening has been shown to be a poor indicator for identifying high-risk patients and is not utilized by the care coordinators. As described by participant “P07,” the high-risk screening is:

“…not beneficial. I mean, they can have a two (score), and it looks like they have no needs, and they could have everything in the book. Or, they could have the high score and it’s not that bad. They don’t need that much, it’s already taken care of, they come in quite a bit. They have homecare, they have transport, they have everything they need… I think it’s another step we have to do, but I don’t see the benefit.” – P07

Third, many ICCs had concerns with team communication. Most of an ICC’s day is spent trying to contact people to arrange appointments and transportation for patients or discuss patient discharge status. 40% of participants reported that they had trouble communicating with doctors and other medical staff, making their jobs difficult. Because there is no set standard of communication between the staff, each staff member must choose their preferred way of communication. This can include, but is not limited to, text messaging, using pagers, phone calls, or built-in messaging features of the EHR. In many cases, the key issue that ICCs face is timely communication. This happened to participant “P12” who said:

“I feel that because I am not getting a thorough report… my progress note is not as clarifying as it could be… like today I called him and asked: are there any discharges that I could start working on? And he gave me the list But sometimes he gives the list late, at like 1:00pm.” – P12

Two subthemes, “Excessive traveling between locations” and “Bad high-risk screening,” were the most common barriers the participants mentioned (70% of participants had these issues), followed by “Lack of real time charting” and “Issues with accessing technology in any given location.” The participants used workarounds to solve these issues. Because the care coordinators need to move back and forth between their offices and patient rooms, they expressed a need for real-time documentation. Ideally, this would enable ICCs to spend more time with patients. This highlights a need for the development of a mobile application to allow real-time documentation at the bedside.

One workaround method to improve documentation efficiency was for ICCs to print out each patient’s “face sheet” (EHR summary) so they can take notes while in the patient room. After visiting their patients, ICCs would then return to their distant office and document necessary information based on hand-written face sheet notes. For many participants, this approach saves them travel time because they don't need to return to their office to document each patient encounter. Another method used by ICCs for improving efficiency was using the computers inside the room to document in while speaking with the patient. In many cases, however, the computers either did not work or were being used by other staff members. This method is also subject to personal preference, as it limits patient interaction.

EHR log analysis

Figures 2 and 3 show the daily volume by an hour of ICCs’ EHR logs. Figure 2 shows the total and view access actions, whereas Figure 3 shows modify, export, and system-related activities. The volume on the y-axis is the sum of the individual volume of all staff ICCs (N = 10). This chart shows the overall volume (red line) and the viewing activities (gray line), which exhibit similar patterns. Overall, participants had most clicks at 8 am followed by a drop at 9–10 am (possibly due to rounding with physicians). Their afternoon activities showed many clicks as well, particularly around 2–3 pm. EHR activity dropped significantly after 5 pm, although there were a few activities that carried into the evening. Figure 2(b) (right) shows modify-, export-, and system-related activities. For example, a clinician can modify clinical notes and export (print) patient education materials. These three activities are separated and illustrated in another chart because their volume (7%) is much smaller than the overall volume. Figure 2(b) shows that the modify-related behaviors exhibit a similar pattern to viewing behaviors. On the other hand, export- and system-related behaviors did not peak around 8 pm. Instead, the participants tended to conduct export-related activities in the afternoon, which is likely due to printing notes and education materials. Another interesting observation is that two of the ten participants modified EHRs after hours (5 pm–7 am), which can be a sign of documentation burden and can negatively impact job satisfaction.^42,43

Figure 2.

Daily volume of participant EHR event logs by all and view access actions.

Figure 3.

Daily volume of participant EHR event logs by modify, export, and system-related activities.

Table 3 shows the distribution of event areas and access types. Participants viewed patient clinical information the most, followed by clinical notes and patient demographics. Participants frequently modified flowsheets and clinical notes, which is consistent with our interview findings. It is worth noting that patient risk screening and psychosocial assessment tools have been incorporated into the institution’s EHR system as flow sheets, contributing to a large amount of modifying behaviors on flow sheet log events. Lastly, participants commonly exported clinical notes and reports, likely for communication purposes with peers and for education purposes with patients.

Table 3.

Participant EHR logs by event areas and access types.

Event Area/Access Type	View	Modify	Export	System	Row Sum	Row Sum (%)
Patient Clinical Info	82,502	1284	145	134	84,065	39.1
Clinical Notes	57,225	4079	1884	--	63,188	29.4
Patient Demographics	36,598	216	--	--	36,814	17.1
Flowsheets	9571	4986	--	--	14,557	6.8
Workflow	5592	--	404	463	6459	3.0
Orders	3533	2	54	1	3590	1.7
Reporting	3056	--	1705	--	4761	2.2
Billing	672	--	--	--	672	0.3
Patient Management	419	--	--	--	419	0.2
Documents	397	--	2	--	399	0.2
Patient appointment	127	--	--	--	127	0.1
Medications	126	--	14	--	140	0.1
Problems	13	2	--	--	15	0.0
Column Sum	199,831	10,569	4208	598	215,206	100.0
Column Sum (%)	92.9	4.9	2.0	0.3	100.0

No data available is indicated by “--”.

Triangulation and interpretation

The combined qualitative and quantitative data clearly showed that ICCs had a routine process yet needed to be flexible to accommodate the dynamics in their daily schedules imposed by travel between their office and patients’ rooms. The participating ICCs had individual differences in their routines but shared a clear workflow pattern. First, the ICCs usually started their day with chart reviews to understand their patients’ progress, followed by rounding with physicians, allowing them to prioritize their patient list with a focus on the patients being discharged soon. The log analysis showed a significant amount of reading and modifying activities between 7 am and 9 am before their rounding at 10 am. The participating ICCs’ afternoons were typically more dynamic. They moved around between patient rooms and their offices, partly due to a large number of cases and lack of proper access to workstations at the bedside. In addition to the long travel time, the ICCs suffered from heavy documentation load and suboptimal communication. The log analysis showed an increased amount of exporting (printing) activities in the afternoon. To overcome documentation barriers, the participating ICCs seemed to develop their own coping strategies to improve efficiency, such as preparing patients’ face sheets or using workstations in or near the patient rooms.

Since the qualitative analysis indicated that the ICCs moved around a lot in the afternoon than in the morning, further analysis of the quantitative EHR log data was conducted with a focus on their location changes in the morning versus the afternoon. The cutoff between the morning and afternoon at 11 am was derived from the findings of the qualitative interviews. The locations were approximated using the workstation names recorded in the EHR logs. As explained in the methods section, the location change for each ICC was defined as the ratio of actual changes to total possible changes in a location sequence. Table 4 summarizes the location change ratio calculated using the event logs. The morning and afternoon time periods were renamed to preparation and bedside to better reflect the nature of the two stages. The preparation stage includes events between 7 am and 11 am (not including 11 am), when ICCs are preparing for their day and reviewing patient charts. The bedside stage includes events between 11 am and 6 pm when ICCs are at the bedside talking to patients and/or discharging them. The results show that ICCs in the bedside stage had a significantly higher location change probability than those in the preparation stage, which confirms our findings in the qualitative analysis.

Table 4.

Location change in the preparation and rounding stage.

	Time Period	Number of logs	Average Change (%)	Median Change (%)
Preparation	7am–11am	141	13	0
Bedside	11am–6pm	151	23	10^a

p-value <0.001 using Kruskal–Wallis test to test for statistically significant differences.

Discussion

This study adopted a concurrent triangulation design to understand ICCs’ clinical workflow and barriers. The triangulation of qualitative and quantitative data allowed the research team to identify workflow patterns and barriers in a higher granularity and understand their context. In addition, the EHR event log analysis provided empirical evidence to support the workflow patterns (i.e., location changes) found in the qualitative analysis, which cannot be done in a single method study. Unfortunately, the log data alone was not able to provide evidence for ICC’s suboptimal communication since these behaviors are not captured in this dataset even though the qualitative study identified this as an issue.^6,13 The study also echoed the concerns in using EHR systems to support teamwork and reducing documentation-related workload as reported in an oncology care coordination study.⁴⁴ The long travel time and frequent location changes were not a common issue identified in the literature, which provides a unique context for use of a Health IT solution.

The results also suggest several design considerations for developing a Health IT solution that supports ICCs’ work. The first consideration is Mobility. Due to extensive travel time, ICCs need real-time access to EHRs at the bedside. Therefore, a Health IT solution would preferably be mobile based. The second consideration is EHR Integration. The solution should be integrated with the institution’s EHR system to allow timely documentation and avoid unnecessary data entry efforts to reduce documentation workload. In our experiment, ICCs, or social workers in general, are often neglected in the initial EHR transition or implementation plan and therefore required to fit themselves into the redefined workflow and data entry requirements. This leads to suboptimal case management and care coordination. A Health IT solution should be designed in an ICC-and-patient-centered manner and focused on facilitating chart review and documentation processes, for example, detecting and highlighting social determinants of health keywords in clinical notes. The third consideration is Team-based Communication. A Health IT solution should allow ICCs to have real-time and asynchronous communication with their coworkers.⁴⁵ Moreover, this team-based communication should be managed and monitored by the information service team to guarantee and protect patient privacy. The fourth consideration is User Adoption. A Health IT solution should be easy to use and able to accommodate individual ICCs’ work and patient communication style, in order to increase user adoption and avoid negative, unintended consequences as a result of using a new Health IT solution.⁴⁶ With these design considerations, a Health IT solution has great potential to streamline the ICCs’ workflow and improve their work efficiency.⁴⁷

As a contribution to the field of mixed-methods research, this study showed that using a concurrent triangulation design with both qualitative interviews and EHR event logs can effectively uncover workflow patterns and be efficient both in time and resources. Workflow data were collected in a timely manner (within a month) and analyzed on both data sources in parallel. Conducting prolonged interviews and/or TMS was not feasible for our study since ICCs have a tight schedule with frequent interruptive phone calls from other team members. This was even less possible during the COVID-19 pandemic. Furthermore, comparing both qualitative and quantitative data created a more comprehensive picture of the ICCs’ workflow barriers and informed Health IT design considerations. Researchers are encouraged to adopt a mixed-methods study design when conducting clinical workflow analysis to gain a deeper understanding of workflow while maintaining a reasonable study time line and resources.

This study has a few limitations. First, the generalizability of our methodology is yet to be tested because it was conducted at a single hospital in a specific setting. Second, some of our findings are similar to those identified in the literature, though in other specialty settings. However, long travel time is a unique issue for ICCs and is a key consideration in our design considerations. Third, the study used vendor-designed activities (event areas in the EHR system) and did not relate these records to actual clinician behaviors. This was not necessary in the current study, however, because the overall counts of activities were enough to explain the ICCs’ workflow patterns. Direct observations will be considered in future studies to verify the event logs and categorize them based on the observed or perceived clinical tasks. Next, because the study sample was not representative, the actual years of service and turnover rate of all ICCs at UCMC are unknown. This information is outside the study’s scope but can be explored in the future. Lastly, the location information (workstation identifiers) in the logs was not mapped to actual building locations. This means that the location change is an approximation and represents the number of location changes. Therefore, it may underestimate the actual travel burden.

The study informs future research in multiple directions, including conducting detailed sequential pattern analysis on the EHR event logs, incorporating location tracking data to better estimate travel time, and implementing a mobile-based informatics solution with Health IT standards (e.g., SMART-on-FHIR) to improve its portability, evaluating the impact of the informatics solution on workflow²⁶ as well as staff and patient satisfaction, and expanding the study to another healthcare institution to demonstrate the effectiveness of the solution.

ICCs play a critical role in care delivery yet have unique challenges to their clinical workflow. This study used a mixed-methods approach to analyze the ICCs’ workflow patterns and barriers in one US institution. We offer four design considerations for the development of a Health IT solution to support ICCs’ work and care delivery. The mixed-methods study design was effective and efficient for our clinical workflow analysis.

Footnotes

Acknowledgments

The authors thank the participating ICCs for their time and effort. The authors thank Ms. Keyin Jin and Ms. Sarah Salomone for their effort on data collection and analysis, as well as Ms. Shwetha Bindhu for her effort on copy-editing. This study was supported by Dr Wu’s start-up funds.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Danny TY Wu

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