Improving the Transition from Hospital to Home for Clinically Complex Children.
As an innovative trend in patient-centered care, the goal of patient navigation is to increase access to care, aid in diagnosis and treatment of multiple disease processes, and improve patient outcomes (Freeman, 2012; Pedersen & Hack, 2010). The concept of patient navigation was originally developed in the 1990s by Dr. Harold Freeman in order to address delays in treatment for socioeconomically disadvantaged individuals in New York City (McVay, 2014). Although many early patient navigator programs have used registered nurses (RNs), programs have since expanded to include a variety of licensed healthcare professionals and community lay workers (Hedlund et al., 2014; McVay, 2014; Pedersen & Hack, 2010). Evidence suggests that patient navigators have been able to effectively reduce barriers to appropriate surveillance and diagnostic resolution, including access and coordination concerns, especially for vulnerable or underserved populations (Baik, Gallo, & Wells, 2016; Freeman, 2012; Krok-Schoen, Oliveri, & Paskett, 2016; McVay, 2014; Natale-Pereira, Enard, Nevarez, & Jones, 2011; Schwaderer & Itano, 2007). However, no uniform template or consistent standards for program creation or navigator role exist (Pedersen & Hack, 2010), with structure and goals differing based on patient population and institutional needs.Nationally, over 15% of U.S. children have special healthcare needs (Data Resource Center for Child & Adolescent Health, 2010). Of these children, 42.1% require increased medical, educational, and mental health services, with over 13% of families reporting 11 hours or more a week spent providing their children's care (Data Resource Center for Child & Adolescent Health, 2010). National trends point to a significant increase in the total number of children identified as medically complex. This may be attributed to increased survival rates of children born with congenital conditions or prematurely (Burns et al., 2010). Such medical complexity is often characterized by high healthcare utilization and an increased need for care coordination (Simon, Mahant, & Cohen, 2012).
Additionally, clinical complexity may complicate the process of discharge from hospital to home, as multiple treatments and specialist appointments are needed (Boykova, 2016). Non-adherence to recommended appointment schedules is common, and may increase risk of readmission and morbidity. A recent study of readmission in Medicaid beneficiaries found that completion of follow-up appointments within seven days of discharge was associated with significant decreases in the risk of readmission in the clinically complex patient population (Jackson, Shahsahebi, Wedlake, & DuBard, 2015).
In the inpatient setting, use of a transition of care intervention to address concerns surrounding discharge teaching and planning may have the potential to improve care coordination for complex patients as well as decrease readmission (Auger, Kenyon, Feudtner, & Davis, 2014). In adult populations, patient navigation may be associated with improved care and health outcomes in patients undergoing treatment for cancer (Baik et al., 2016; Freeman, 2012; Krok-Schoen et al., 2016), among other life-threatening conditions (Metsch et al., 2016). However, no such studies currently exist in the pediatric population.
As patient navigation programs continue to evolve and spread (Hedlund et al., 2014), a gap exists in rigorous evaluation of the impact and effectiveness of such programs for the clinically complex pediatric population. The purpose of this article is to describe the integral role RN navigators play for clinically complex children as they transition from hospital to home in one acute care pediatric hospital. The authors aim to describe the process of building a patient navigation program in a pediatric acute care setting, outline the function of the patient navigation program, describe the pediatric population using these services, and evaluate the outcomes of the program, including 30-day readmission rates and follow-up appointment completion rates.
Building a Patient Navigation Program
Recognizing gaps in the transition of care from the hospital to home for clinically complex patients and the need to facilitate access to outpatient medical providers, an East Coast, stand-alone, Magnet[R]-designated, pediatric hospital began the process of creating a patient navigation program in 2013. This hospital serves a mixture of patients from both urban and rural areas, with over 50% of the admitted population using Medicaid insurance. With 200 inpatient beds, the stand-alone pediatric hospital discharges over 10,000 pediatric patients a year. The clinically complex patient population served by the hospital was identified to have difficulties with coordinating care after discharge. As such, the program was initially conceptualized within the context of a continuous improvement initiative, and it evolved into the development of a patient navigation program. This hospital had never attempted such a patient navigation design but wanted to think creatively to assist families with children who required multiple medical specialty care.
Success of the new service hinged on the ability of the navigator to be able to critically evaluate complex medical information, assess the abilities of families to understand and follow instructions, plan the sequential order of appointments, and implement strategies to help families successfully care for their child. Due to the critical appraisal needed to successfully evaluate patient needs and schedule appointments appropriately, RNs were hired as navigators, with bilingual lay navigators recruited for interpretive services. As the patient navigation team began to grow, communication tools, such as departmental phone, email, fax, and electronic medical record order sets, were established. To advertise the new patient navigation services, marketing materials were created, and partnerships were established with external referring agencies, such as ambulatory practices, community physicians, and community nursing agencies. Additionally, the navigation team implementation was an agenda item at internal management meetings and physician resident meetings, which served as the primary source of inpatient referral. The patient navigation team was quickly recognized as a valuable resource, and patients were referred for a multitude of access needs. These included scheduling multiple same-day appointments, improving access to clinicians with long wait times, and addressing transportation difficulties.
Patient Navigation Workflow
To provide a more streamlined, efficient approach to discharge for clinically complex children, the patient navigation program was implemented in inpatient units. In the hospital setting, the clinical team may request patient navigation services using the electronic health record (EHR) referral system, in which an alert is sent directly to a member of the patient navigation team, becoming part of a patient's medical record. Referrals may be initiated by any provider who works directly with the patient, although most referrals are initiated by nurses, physicians, or social workers. Clinical teams are instructed to refer patients who meet the following criteria: 1) require three or more appointments after discharge, 2) clinical or psychosocial complexity based on the clinician's expertise, or 3) providers' clinical assessment warrants referral due to concerns for post-discharge care management. Ordering providers were educated by attending the monthly Resident Education Meetings, as well as one-on-one instruction as needed. Most importantly, the constant collaboration between navigators and the multidisciplinary inpatient team assured that the service was utilized.
After receiving a request from a clinical team member, RN navigators meet caregivers at the bedside, assess for transportation needs, and schedule follow-up appointments. Although some families would only require one visit, this number was variable and dependent on patient needs. Every effort is made to schedule primary, specialty, procedure, and testing appointments prior to discharge by scheduling directly into EHR templates, or serving as a proxy and contacting administrative staff by telephone or email. In the case of evening or weekend discharges, families are contacted, and appointments are made within two days of discharge. A personal connection between the navigator and the family is established, and department contact information is shared. RN navigators share their assessment of patient needs with the clinical team and communicate any barriers to successful transition from hospital to home, including insurance, language, and transportation concerns. RN navigators advocate for patients in a variety of ways, including working proactively to connect the patient and family to appropriate resources. Additionally, navigators work with families to clarify conflicting clinical information at the time of discharge to smooth the transition process. At the time of discharge, families are also provided with the navigation team business card so RN navigators could be directly contacted to assist families to reach resolution of questions or concerns.
Methods
Setting and Sample
The patient population studied received inpatient care at an East Coast, stand-alone, Magnet[R]-designated, pediatric hospital. Patients age 0 to 20 years who were admitted to inpatient units in the hospital of interest for a minimum of one night between January 1, 2015, and December 31, 2015, and were subsequently provided patient navigation services either in person or via follow-up telephonic communication within two days of discharge, were included in the study. A total of 398 unique patients met inclusion criteria, with 422 total patient admissions.
Analytical Approach
A retrospective review of de-identified patient data from January 1, 2015 to December 31, 2015, was completed. Demographic data were identified from administrative records, including billing and registration data. Clinical characteristics and diagnoses were drawn from the Epic[R] EHR. Patient data were stored de-identified on a secure, password-protected server.
Primary outcomes of interest included patient demographic information (age, sex, race, language, primary admitting diagnosis, insurance status, number of comorbidities), 30-day all-cause readmission rate, "no show" appointment rate, and source of patient referral. Co-morbidities or co-diagnoses were extracted from the patient's active problem list and represent a wide range of medical conditions, both acute and chronic. Readmission rate was determined by evaluating whether a patient was readmitted to the same hospital within 30 days of discharge. "No show" rates were based on appointments directly scheduled into the EHR by navigators, representing a subset of total appointments. Standard descriptive statistics were used to describe patient demographics and outcomes of interest. The research study was reviewed and approved through the institution's IRB.
Results
Demographics
Demographic characteristics of the population using patient navigation services are outlined in Table 1. A total of 398 unique patients comprised the study sample, with a total of 422 inpatient encounters reported during the study period. Many patients used services more than once in the study year, accounting for the discrepancy between unique patients and total visits. On average, the sample patient population had a mean age of 7.4 years (SD: 6.6 years) and ranged from 0 to 20 years of age. Patients from 0 to 1 year of age comprised 34.1% of the sample. Approximately 52.5% of patients were female. The sample was predominantly White (52.0%), with 30.2% of patients identified as Black/African American. English was found to be the predominant primary language, with Spanish being the second most common primary language (6.5%). Although patient primary admission diagnosis varied greatly, the top five diagnoses were convulsions, dyspnea/respiratory abnormality, pneumonia, fever, and asthma, as identified by ICD-9 or ICD-10 codes. Most patients identified Medicaid as their primary insurance (49.2%), closely followed by commercial insurance (44.7%). Representing a highly complex patient population, the sample had an average of 12 co-morbidities or co-diagnoses (SD: 10). The number of reported co-morbidities or co-diagnoses ranged from 0 to 58.
Readmission Rates
For a total of 422 inpatient admissions from January 1 to December 31, 2015, the mean 30-day readmission rate was 15.9% (see Figure 1). This rate fluctuated by month and ranged from 5% to 23%. Some seasonality appeared to be associated with readmission rates, with decreased admissions noted during summer months. The highest rates of readmissions were noted in January, February, and March.
Figure 1. Patient Navigation Readmission Rate, 2015 (n=422) (a) Month Jan 23% Feb 20% Mar 22% Apr 16% May 5% Jun 10% Jul 16% Aug 16% Sep 20% Oct 23% Nov 15% Dec 5% (a) For the 422 admissions, rate of all-cause readmission within 30 days is reported, by month. Note: Table made from line graph. Figure 2. Patient Navigation 'No Show' Rates, 2015 (n=326) (a) Month Jan 0% Feb 0% Mar 22% Apr 11% May 15% Jun 12% Jul 3% Aug 18% Sep 6% Oct 19% Nov 12% Dec 35% (a) A total of 326 appointments, directly scheduled by patient navigator staff, are reported above. This represents a subset of the total number of patient appointments. Note: Table made from line graph.
'No Show' Rates
Of the 398 unique inpatients who used the patient navigation service, a total of 326 post-discharge follow-up appointments were directly scheduled into provider templates by patient navigators (see Figure 2). This number does not include appointments that patient navigators scheduled by calling offices and working with schedulers, or those appointments that may have been scheduled and subsequently canceled. "No show" was defined as a patient failing to complete a scheduled appointment. Of the 326 total appointments (encompassing primary and specialty care), 42 (12.9%) were "no shows." The rate of "no show" appeared to vary by month, with the highest rate (35%) occurring in December (see Figure 2). Unfortunately, due to data limitations, the percentage of "no shows" that occurred as a consequence of readmission is unable to be calculated. Qualitative review of patients who failed to complete a scheduled appointment did not reveal any existing pattern, with varied reasons contributing to "no shows."
Sources of Referral
In addition to those patients who were provided patient navigation services while receiving care as an inpatient, many patients are also referred to the patient navigation team from outpatient and community settings. From January 1 to December 31, 2015, a total of 4,046 referrals were received by the patient navigation team from all sources (see Figure 3). A retrospective review of referral source identified hospitalaffiliated outpatient practices, including primary and specialty care, as the highest source of referral (56%). At 27%, family-initiated referrals were the second most frequent method of requesting patient navigation services. Referrals from outpatient (primary and specialty care) and inpatient (acute care hospitals) sources in the community setting accounted for 5% of the total referral volume.
Figure 3. Sources of Referral to Patient Navigation Services, 2015 (n=4,046) (a) Outpatient--Hospital Affiliated 2,270 (56%) Family Initiated 1,087 (27%) Inpatient--Hospital Affiliated 464 (12%) Outpatient--Community 179 (4%) Inpatient--Community 46 (1%) (a) Total referrals represent unique encounters, but not necessarily unique patients. Duplicates may exist. Figure highlights both inpatient and outpatient services. Note: Table made from pie chart.
Discussion
This article provides an overview of one hospital's journey to develop a program to address the needs of clinically complex children that require intensive intervention. The study was conducted to describe the process of building a patient navigation program in a pediatric acute care setting and to evaluate program outcomes in clinically complex patients. The study found that patient navigation RNs provided services to a racially and culturally diverse patient population, a high level of complexity existed within the patient population, 30-day readmission rates were higher than the general pediatric inpatient population, and the "no show" rate for patients in the study period was 12.9%.
After analysis, the sample population in our study was racially and culturally diverse, with over 8% of the sample speaking a primary language other than English (see Table 1). Additionally, those patients who received services had a high level of complexity, with an average of 12 comorbidities or co-diagnoses per patient. This complexity is unique. Many adult navigation programs tend to focus on assisting a patient through a single diagnosis, such as cancer. The clinical diversity and complexity of the patient population described supports the need for RN-managed navigation services because a wide range of clinical conditions may need to be evaluated and addressed (Pedersen & Hack, 2010). This reflects recent work in ambulatory care settings and patient-centered medical homes, where RN competencies are being developed for care coordination and transition management (CCTM[R]) (Haas, Swan, & Haynes, 2013).
Unexpectedly, of 422 total admissions across the 398 patients, there were 245 unique primary admission diagnoses. However, the top five primary admitting diagnoses identified (convulsions, dyspnea/respiratory abnormality, pneumonia, fever, and asthma) align with the top causes of readmission in children nationally (Berry et al., 2013). Additionally, these primary diagnoses mirror institutionwide data of the top causes of readmission. This suggests that despite their differences in complexity, the patient navigation population is similar to the general population in cause of admission.
At the national level, the 30-day unplanned readmission rate is 6.5% compared to 15.9% for the clinically complex population assisted by patient navigation services reported here (Berry et al., 2013). However, that readmission rate may reflect planned readmissions as well as unplanned readmissions. Additionally, clinically complex patients often have high healthcare use (Simon et al., 2012), and are at increased risk of hospitalization due to common illnesses. Therefore, a 30-day readmission rate higher than the national average is expected. However, additional study is needed to find rates of readmission for comparable patient populations with and without the use of navigation services.
Clinically complex patients are often discharged with many follow-up appointments; therefore, a "no show" rate of 12.9% is comparable to or lower than national rates. A 2010 study examining the impact of reminders on "no shows" found that in the absence of appointment reminders, the rate of missed appointments in outpatient practices can be as high as 23.1% (Parikh et al., 2010). In the program described here, patient navigators assess for transportation needs for every patient to identify those at risk of missing appointments. This is especially important when discharging a patient home with multiple follow-up appointments because transportation is one of the most prevalent barriers during transition of care (Syed, Gerber, & Sharp, 2013).
Although the described novel patient navigation program enjoys positive reviews from family and strong administrative support, barriers to optimum functioning and opportunities for improvement exist. Due to institutional norms and resource constraints, certain limitations in the scope of navigators' work existed, and these have been addressed as the program has grown and evolved. The cultural change necessary to allow RN navigators to directly schedule follow-up appointments represents one such barrier. Although direct scheduling into provider templates became available for some institution-affiliated practices, a substantial number of practice areas remain in which RN navigators must work through an intermediary to schedule appointments. Expanding the ability of navigators to schedule directly could further streamline the process for all institutions implementing a patient navigation program.
As the need for patient navigation continues to grow, additional resources are needed to meet the demand of a diverse population. As evidenced by this analysis, language concordance may pose a challenge for effective coordination and transition services. Language concordance between the navigator and the patient, or access to high-quality interpretive services, is imperative to creating a personal relationship and effectively communicating with patients and families. Nationally, the need to provide medical services in a culturally competent manner is receiving growing recognition (The Joint Commission, 2007), and studies support the importance of language for successful transitions of care (Karliner et al., 2012; Rayan, Admi, & Shadmi, 2014). Recruitment of RN navigators who can provide these interpreter services should be addressed while building a program.
Increased use of patient navigators in acute care settings is likely driven in part by the increasing acuity of patients and shortened timeframe in which bedside nurses must provide education and discharge planning. Despite the interest in these programs, a lack of definition surrounding the role of the navigator remains, as well as outcomes of these programs in the pediatric population. While a template approach may not be appropriate due to the unique patient population served, as well as the context in which care is delivered, standardization of collected outcomes metrics may strengthen navigation programs. Additionally, clarity surrounding the roles and competencies of RN navigators would be beneficial to the growth of the field. Research focused on demonstrating the impact of such programs is needed.
Limitations
This work represents a novel program in a single institution, and as such, may not be generalizable. Due to the retrospective nature of this work, data may be inaccurate or missing. Additionally, as the definition of clinically complex is multifaceted, it is difficult to clearly define a group for clinical comparison or a control group. While 49.2% of the patient population identified Medicaid as their primary source of insurance, some individuals may have had other sources of secondary insurance as well. Additionally, although 30-day readmission to the same hospital is reported, this does not capture a readmission that occurs at an outside hospital. While "no-show" rates for appointments directly scheduled into the EHR templates by navigators are reported, this only represents a portion of the total number of patient appointments. The decision to only report on appointments directly scheduled into templates by navigators is due to the ability to track these appointments for completion, which the authors were unable to do for appointments not directly scheduled. However, evaluation of this subset of appointments serves as a way to estimate the over-all adherence to scheduled follow-up appointments.
Conclusion
In conclusion, the patient navigation program described here represents a novel, systematic improvement to streamline the transition of care process for clinically complex children. While such programs are growing in number, there is little clarity around program design or functionality. After analysis, the inpatient population assisted by patient navigation is highly complex, with a high average number of co-morbidities, and as such, may need additional support through transitions of care.
Although a hospital admission is frequently a stressful and difficult time for patients and families, clinically complex children may be at increased risk of poor outcomes after discharge due to their frequent need for multiple follow-up specialist appointments, among other concerns. A robust patient navigation program may provide support and scheduling services needed by parents and families to help with the transition of care from the hospital to home. Other healthcare systems may use the model provided here to guide the growth of their own programs.
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Danielle Altares Sarik, Mary Pat Winterhalter, and Christina J. Calamaro
Danielle Altares Sarik, PhD, CPNP-PC, RN, is a Research Nurse Scientist, Nursing Research Department, Nicklaus Children's Hospital, Miami, FL.
Mary Pat Winterhalter, MS, RN, NE-BC, is Director of Nurse Navigation, Fox Chase Cancer Center, Philadelphia, PA.
Christina J. Calamaro, PhD, CRNP, RN, is a Senior Nurse Scientist and Director of Research and Evidence-Based Practice, Children's Healthcare of Atlanta, Atlanta, GA.
Acknowledgements: The authors wish to thank Steven Vijayan for his help with data management, cleaning, and analysis.
Funding: This work was supported by the Cardinal Health Foundation, Dublin, Ohio [E3 Patient Safety Grant].
Table 1. Patient Navigation Demographic Characteristics (n=398) Characteristic n(%) (a) Age (years) 0 to 1 136 (34.1) 2 to 5 50 (12.6) 6 to 11 76 (19.1) 12 to 20 136 (34.2) Sex (female) 209 (52.5) Race White/Caucasian 207 (52.0) Black/African American 120 (30.2) Other 71 (17.8) Primary language English 365 (91.7) Spanish 26 (6.5) Haitian/Creole 3 (0.8) Arabic 2 (0.5) Vietnamese 2 (0.5) Primary admitting diagnosis (b) Convulsions, unspecified or other 20 (5.0) Dyspnea/Respiratory abnormality 16 (4.0) Pneumonia, organism unspecified 11 (2.8) Fever, unspecified 8 (2.0) Asthma, unspecified, with status asthmaticus 7 (1.8) Primary Insurancec Medicaid 196 (49.2) Commercial 178 (44.7) Self-pay 13 (3.3) Tricare 7 (1.8) Medicare 2 (0.5) In-kind care 2 (0.5) Co-morbidities, mean (SD) 12 (10) (a) Percentages may not add to 100 due to rounding or missing data. (b) A total of 245 primary diagnoses were identified using ICD-9 and ICD-10 codes, only the top five diagnoses are listed here.
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| Author: | Sarik, Danielle Altares; Winterhalter, Mary Pat; Calamaro, Christina J. |
|---|---|
| Publication: | Pediatric Nursing |
| Article Type: | Report |
| Geographic Code: | 1USA |
| Date: | Nov 1, 2018 |
| Words: | 4609 |
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