Abstract
Objectives:
The objective of this study is to identify contextual access factors to home and community health services for end-of-life support for older adults with serious life-limiting illness, as well as determine if access to home and community services is associated with healthcare utilization.
Design:
This study includes an environmental scan, grey literature review, qualitative interviews, and healthcare utilization analysis. This study is a sub-project of the Grudzen et al. Primary Palliative Care for Emergency Medicine (PRIM-ER) study.
Settings/Location:
Analysis will include data collection from 17 health systems implementing the PRIM-ER intervention.
Subjects:
For the qualitative interviews, one emergency medicine (EM) physician and one EM nurse will be interviewed from each of the 17 participating health systems. For the healthcare utilization analysis, we will use the Senior Care Services Scale (SCSS), American Hospital Association Annual Survey of Hospitals (AHA-ASH), and Medicare claims for all ED visits for serious illness who present at each participating health system.
Outcome Measures:
The contextual analysis will obtain data on home and community services such as hospice, home health services, assisted living, integrative medicine services, etc., available around each health system’s highest volume ED, federal and state regulations influencing access to services, as well as EM provider perspectives on access to services. The healthcare utilization analysis will determine if SCSS scores, which measure service availability, are associated with healthcare usage. High or low SCSS scores are determined by comparing health system service availability on the AHA-ASH to the national median SCSS value.
Keywords: Palliative care, Emergency Medicine, Integrative Medicine, Health Systems
Introduction:
Emergency medicine developed as a specialty to treat the acutely ill and injured, yet emergency departments (EDs) increasingly care for older adults with multiple comorbid conditions who present for acute exacerbations of chronic illness. Visits to the ED by older adults, as well as the number and rate of admissions to the Intensive Care Unit, are increasing as a proportion of all ED visits.1,2 The proportion of the United States (US) population 65 years and older will continue to grow, and EDs will see an increase in both the number of older adults and the complexity of care they are required to provide.3 However, emergency care has not fully adapted to the needs or goals of seriously ill older adult patients who prefer to have care delivered at home, or to, “age-in-place.”4–7
Until recently, little attention has been paid to goal-concordant care in the ED for older adults with serious illness. The default treatment plan is to provide intensive care that favors life-sustaining therapies and in-patient services. However, most patients prefer to die at home, and the proportion of patients dying at home compared to an acute care setting has increased over the past several decades.8,9 Providing home-based end-of-life services increases the probability of dying at home.10 In addition, there is a reduction in overall health services costs for those receiving home-based end-of-life care.10 Getting older adults out of the hospital and into home and community health services to support end-of-life needs may improve care satisfaction and relieve the burden of older adult end-of-life care in the ED and on the US healthcare system.
In previous studies, access to home and community health services is often associated with patient and/or neighborhood characteristics.11–13 However, less is known regarding state, regional, and health system characteristics that may influence access to home and community health services. Even fewer studies consider access to complementary and integrative medicine (CIM) services (Naturopathic, Chiropractic, Acupuncture, etc.). This is important given the high symptom burden at the end of life and growing interest in CIM among older adults.14 CIM can play an important role in palliative care15,16, and the prevalence of CIM is high among populations at risk for impaired health care access.17,18 However, there is wide variation in licensure and scope of practice for integrative practitioners. For example, naturopathic doctors (NDs) provide integrative options for disease and symptom management using a variety of techniques including nutrition, dietary supplements, lifestyle modifications, intravenous nutrients, physical medicine, mind/body medicine, and pharmaceuticals when necessary. However, only 22 US states, the District of Columbia, as well as the US territories of Puerto Rico and the U.S. Virgin Islands provide licensure for naturopathic physicians.19 For the states that license NDs and provide a legal means for NDs to practice, there is wide variation in the modalities permitted to define their scope of practice. This variation influences available services that hold the potential to support older adults with serious life-limiting illness.
Creating a comprehensive approach to assess access factors to home and community end-of-life services is needed to understand the barriers and facilitators currently in place. We propose a multi-method contextual analysis of factors that effect access to home and community health resources for older adults with serious life-limiting illness. This study is a sub-project of the Grudzen et al. Primary Palliative Care for Emergency Medicine (PRIM-ER) study.20 PRIM-ER is an education, training, and technical support quality improvement intervention focused on the delivery of palliative care in the ED. It utilizes a cluster-randomized stepped wedge design among 33 EDs within 17 health systems across the US. The intervention consists of four core components at each ED: 1) evidence-based multidisciplinary primary palliative care education, 2) simulation-based workshops on communication in serious illness, 3) clinical decision support, and 4) provider audit and feedback. PRIM-ER is a pragmatic trial aimed to help address the needs of older adult end-of-life patients and serves as the impetus for this protocol.
Study Aims:
The primary aim of this study is to identify contextual access factors to home and community health services for older adults with serious life-limiting illness. We will accomplish this aim by conducting an environmental scan, grey literature review, and qualitative interviews with EM physicians and nurses. The secondary aim is to determine if access to home and community resources as measured by the Senior Care Services Scale (SCSS) is associated with healthcare utilization at the end-of-life. We will accomplish this aim by comparing access factors to home and community services with Medicare claims data.
Methods and Analysis
Services of Interest and Study Location:
The home and community health services of interest include assisted living, skilled nursing care, adult day care, home health services, retirement housing, intermediate care, hospice, palliative care services, rehabilitation, physical and occupational therapy, as well as integrative medicine services such as naturopathic medicine, chiropractic medicine, acupuncture, and massage therapy. Relevant additional services identified during the contextual analysis will also be included.
Our analyses will include data collection from the 17 health systems included in the PRIM-ER intervention, listed in Table 1. Each health system has one or multiple ED departments enrolled in PRIM-ER. For sites with multiple EDs enrolled in PRIM-ER, our analyses will focus on the ED within the health system with the highest ED patient volume per year based on Medicare claims (primary health system ED).
Table 1.
PRIM-ER Enrolled Health Systems, Sites, and Geographical Location
| Health System | Site | Location |
|---|---|---|
| Allegheny Health Network | ||
| Allegheny General Hospital* | Pittsburgh, PA | |
| Baystate Health | ||
| Baystate Franklin | Greenfield, MA | |
| Baystate Medical Center* | Springfield, MA | |
| Beaumont Health System | ||
| Beaumont Royal Oak* | Royal Oak, MI | |
| Beaumont Troy | Troy, MI | |
| Brigham and Women’s/Dana Farber Cancer Institute | ||
| Brigham and Women’s Faulkner | Boston, MA | |
| Brigham and Women’s Hospital* | Boston, MA | |
| Christiana Care Health System | ||
| Christiana Hospital* | Newark, DE | |
| Henry Ford Health System | ||
| Henry Ford Fairlane | Fairlane, MI | |
| Henry Ford Hospital* | Detroit, MI | |
| Henry Ford West Bloomfield | West Bloomfield, MI | |
| Icahn School of Medicine at Mount Sinai | ||
| Mount Sinai Beth Israel* | New York, NY | |
| Mount Sinai Hospital | New York, NY | |
| Mount Sinai West | New York, NY | |
| Mayo Clinic Health System | ||
| Mayo Clinic, St. Mary’s* | Rochester, MN | |
| Mayo Clinic Austin-Albert Lea | Austin/Albert Lea, MN | |
| Mayo Clinic Health Mankato | Mankato, MN | |
| NYU School of Medicine | ||
| Bellevue Hospital Center* | New York, NY | |
| NYU Langone Hospital – Brooklyn | Brooklyn, NY | |
| NYU Winthrop | Mineola, NY | |
| Ochsner Health System | ||
| Ochsner Medical Center* | New Orleans, LA | |
| The Ohio State University | ||
| Wexner Medical Center* | Columbus, OH | |
| University of California, San Francisco | ||
| UCSF Medical Center | San Francisco, CA | |
| Zuckerberg San Francisco General* | San Francisco, CA | |
| University of Florida Health | ||
| UF Health Kanapaha Hospital | Gainesville, FL | |
| UF Health Shands Hospital* | Gainesville, FL | |
| UF Health Springhill Hospital | Gainesville, FL | |
| University of Pennsylvania Health System | ||
| Hospital of the University of Pennsylvania* | Philadelphia, PA | |
| Pennsylvania Hospital | Philadelphia, PA | |
| Penn Presbyterian Medical Center | Philadelphia, PA | |
| University of Texas | ||
| MD Anderson* | Houston, TX | |
| University of Utah Health | ||
| University of Utah Hospital* | Salt Lake City, UT | |
| Yale New Haven Health System | ||
| Yale New Haven Hospital* | New Haven, CT |
CA = California; CT = Connecticut; DE = Delaware; FL = Florida; LA = Louisiana; MI = Michigan; MN = Minnesota; NJ = New Jersey; NY = New York; OH = Ohio; PA = Pennsylvania; TX = Texas; UT = Utah
Indicates primary health system. Health system emergency department (ED) with highest per year volume of ED patient visits, based on Medicare claims
Aim 1: Identify contextual access factors to home and community health services for older adults with serious life-limiting illness
Environmental Scan
We will conduct an environmental scan for each participating health system to identify the home and community services available within a 30-mile radius of a health system’s primary ED (Table 1). Environmental scanning was originally developed for business, but has become more prevalent in health services research.21 There is no definitive definition or methodology for environmental scanning, but in general scanning includes “seeking, gathering, interpreting, and using information from the internal and external environments of an organization to inform strategic decision-making, and identify emerging critical issues facing healthcare.”21 We will implement the 4-mode strategy developed by Choo (1999) which includes 1.) undirected viewing, 2.) conditioned viewing, 3.) enacting, and 4.) searching.22 We selected a 30-mile search radius considering previous studies using the same search radius,23,24 as well as average distances traveled by US adults for various health services depending on rural vs urban location.25–27 We will document the type of services available (ex. home hospice, acupuncture, etc.), number/amount of each service within the search area (ex. number of acupuncture practices and number of providers/practice), and distance of each service from the primary health system ED (Table 2). The state and US region in which each health system resides will also be documented to compare services available among different geographic regions. The US geographic regions will be identified per the US Census Bureau-Census Regions and Divisions of the United States.28
Table 2.
Study Aims, Methodology, and Analyses
| Aim | Research Method | Analyses |
|---|---|---|
| Aim 1: Identify Contextual Access Factors to Home and Community Health Resources | Environmental Scan | •Type of Services Available •Number/Amount of Services Available •Services Distance from Health System ED •US State •US Region |
| Grey Literature Review | •State and Federal Regulations •ED Volume •Health System Ownership •Patient Demographics |
|
| Qualitative Interviews | •Attitudes Regarding Services •Beliefs Regarding Services •Knowledge of Services •Demographics ○Age ○Gender ○Years in Practice ○Percent of Patients > 65 years old ○Board Certification ○License |
|
| Aim 2: Determine if Access to Home and Community Services is Associated with Healthcare Utilization | Healthcare Utilization Analysis | Independent Variables: •Senior Care Services Scale-Post-Acute Community Care Taxonomy Classification* Dependent Variables**: •ED revisits •Inpatient Days •Home Health Use •Hospice Use |
RIF= Research Identifiable File/Site Administrative Revenue Codes
ES = Environmental Scan
Services include skilled nursing, intermediate care, other long-term care, assisted living, retirement housing, adult day care and home health services. Data source is the American Hospital Association (AHA) Annual Survey of Hospitals
Instrument/Coding, Source, and Timing for Dependent Variables: ED Revisits (Count, In- and Outpatient Research Identifiable Files (RIF), Up to 6 months from index ED visit) Inpatient Days (Count, Inpatient RIF, Up to 6 months from index ED visit) Home Health Use (Yes/No, Home Health RIF, Up to 6 months from index ED visit) Hospice Use (Yes/No, Hospice RIF, Up to 6 months from index ED visit)
The search strategy for the environmental scan includes review of professional medical organizations/associations’ provider or services databases (ex. American Association of Naturopathic Physicians, National Association for Home Care and Hospice, etc.) to identify services available in the search area of interest. In addition, we will conduct web searches to locate available services. Two independent scans by separate members of the research team will be completed and compared. Irrelevant services that are not expected to provide any potential benefit for older adult end-of-life support (ex. pediatric medical practice) will be excluded. For organizations with questionable relevancy, an additional review of the organization’s website will be completed to identify if they provide any services to support end-of-life care. Final decision on the inclusion of organizations with questionable relevance will be made using the consensus decision making technique.29 A census report with the services available within each health system, state, and US geographic region will be created. Descriptive statistics will be used to compare services per geographic region.
Grey Literature Review
In addition to the environmental scan, we will also conduct a grey literature review to collect information on federal and state regulations related to access to home and community services. We will document which states provide licensure for CIM services, and variations in state laws regulating scope of practice for integrative medical providers. We will also document state and federal regulations for hospice, home care, and supportive care organizations that may impact access to these services.
The search strategy for the grey literature review will reflect the recommendations from Paez 2017 to balance search specificity and sensitivity without being overly inclusive.30 We will review state and federal laws, as well as related reports, conference materials, and general publications. We will use a combination of resources for our search, including grey literature databases (OpenGrey, WONDER, SCOPUS, Grey Matters, Grey Literature Report, National Technical Information Service, PsycEXTRA, Web of Science, Zetoc), unpublished clinical trials (ClinicalTrials.gov, Cochrane Central Registry of Controlled Trials, World Health Organization International Clinical Trials Platform) conference papers (Conference Papers Index), dissertations, theses, and academic papers (ProQuest Dissertation & Theses Global, WorldCatDissertations) and web searches (Google, Google Scholar, Mednar).
Data from the grey literature review will be categorized at the regional, state, and health system level. Data will be coded and collated per topic of interest. For example, information on state law related to naturopathic medicine in Pennsylvania will also apply to the Northeast US region, Pennsylvania state, and Allegheny Health and University of Pennsylvania health system subcategories. A combination of descriptive statistics, narrative description, and thematic coding will be used to summarize and present the data. This data will also be added to the census report developed during the environmental scan.
In addition to the environmental scan and grey literature review, we will obtain data from the independent variables collected through the PRIM-ER intervention, which are described in the PRIM-ER protocol.20 Specifically, we will collect information on ED volume, health system ownership (non-profit, government, etc.) and patient demographics for each health system. This information will be incorporated into the census report created from the environmental scan and analyzed using descriptive statistics to examine differences in access factors among various health systems and geographic regions.
Qualitative Interviews
We will interview one emergency medicine (EM) physician, and one EM nurse from each participating health system to capture the perspective of EM providers on access to home and community health services for end-of-life care. Interviewees will be a designated physician or nurse champion from the PRIM-ER study who served in a leadership role for the implementation of PRIM-ER at their site. Interviewees will be invited to participate via email by the PRIM-ER program manager, author AC. Author JH will schedule and conduct all interviews. Interview notes will be collected in real time and debriefing sessions will be completed following every third interview (AC and JH). Interviews will be completed and recorded using ZOOM teleconferencing and transcribed verbatim. Transcriptions and other relevant data will be stored on NYU’s secure database system, REDCap. The main subsections of the interview guide will include 1.) provider attitudes, beliefs, and knowledge regarding home and community health services for end-of-life care, and 2.) barriers and facilitators to home and community health services within, and external to, their ED and health system. See supplemental data file 1 for the qualitative interview guide. Additional demographic data including age, gender, board certification, licensure, and years in practice will be collected from the independent variables obtained via the PRIM-ER study.20
The interview recordings will undergo thematic coding. AC and JH will develop deductive codes based on the interview guide, and inductive codes based on salient topics identified during review and coding of the transcripts. We will compare codes across all transcripts during the final analysis phase and condense codes into meaningful categories. We will use Dedoose software31 to facilitate coding as well as cross-investigator and cross-interview analyses. We will also develop data matrices to highlight prominent themes related to facilitators and barriers to home and community health services.
Aim 2: Determine if access to home and community resources as measured by the Senior Care Services Scale (SCSS) is associated with health care utilization at the end-of-life.
We will examine associations between access to home and community health services and healthcare utilization for the 17 health systems included in the PRIM-ER study using the Senior Care Services Scale (SCSS) developed by Arbaje et al (2015).32 The SCSS was developed to identify health services relevant to the care of older adults. Specifically, the SCSS post-acute community care (SCSS-PA) taxonomy classification includes skilled nursing, intermediate care, other long-term care, assisted living, retirement housing, adult day care and home health services. These services will serve as the independent variables for our analysis. The availability of these services for each health system will be determined using the American Hospital Association (AHA) Annual Survey of Hospitals (ASH) and supplemented by data obtained via the environmental scan for this study.33
To calculate an SCSS-PA score, we will mirror the analysis developed by Arbaje et al. (2017).34 We will create binary variables (yes/no) from the ASH data to assign to each service of interest within each health system, depending on availability. SCSS scores will be developed through factor analysis studying the variability of the PA services. Each system will be rated as “Low-SCSS-PA,” or “High-SCSS-PA.” To determine high or low SCSS-PA scoring, we will compare PA services availability to the national median value, with high scores being above the national median, and low scores below national median.
Healthcare utilization data will be obtained from Medicare claims data and serve as the dependent variables for our analysis. These variables include ED revisits (count), inpatient days (count), home health use (Yes/No), and hospice use (Yes/No). We developed these measures of healthcare utilization based on the Dartmouth Atlas Decedent Cohort Care Intensity Measures to monitor the quality of end-of-life care in Medicare patients with serious chronic illness.37,38 We will cross reference each health system’s revenue center codes with Medicare claims data to determine the type and number of visits or inpatient days.35,36 The measures will be collected in the 6 months from a patient’s index ED visit. The index ED visit will be defined as the first ED visit to one of the 33 PRIM-ER ED facilities during which the beneficiary has 12 months of prior inpatient, outpatient, or carrier claims consistent with a Gagne Index >6, or >30% mortality. We will conduct bi- and multivariable regression analyses to determine if SCSS-PA scores are associated with differences in healthcare utilization. Covariates for our analysis will include patient and hospital characteristics collected during Aim 1 and will be supplemented by review of the covariates implemented for the analysis in Arbaje et al. (2017).34
Dissemination
We have a robust dissemination plan outlined including circulating findings at academic conferences, asking interviewees to participate in the manuscript development and review process, as well as disseminating findings to senior leadership at each of the enrolled PRIM-ER institutions.
Supplementary Material
Acknowledgments
Funding
This work is supported within the National Institutes of Health (NIH) Health Care Systems Research Collaboratory by the NIH Common Fund through cooperative agreement [U24AT009676] from the Office of Strategic Coordination within the Office of the NIH Director and cooperative agreement [UG3/UH3 AT009844] from the National Institute on Aging. Support was also provided by the NIH National Center for Complementary and Integrative Health Administrative Supplement for Complementary Health Practitioner Research Experience through cooperative agreement [UH3 AT009844]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Conflict of Interest
The authors have no conflict of interest to declare.
Contributor Information
Jacob D. Hill, Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 227 East 30th Street, New York, NY, 10016.
Allison M. Cuthel, Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 227 East 30th Street, New York, NY, 10016.
References
- 1.Niska R, Farida Bhuiya FACEP;, Xu J. National Hospital Ambulatory Medical Care Survey: 2007 Emergency Department Summary.; 2007. http://www. Accessed June 17, 2019. [PubMed]
- 2.Mullins PM, Goyal M, Pines JM. National Growth in Intensive Care Unit Admissions From Emergency Departments in the United States from 2002 to 2009. Gerson L, ed. Acad Emerg Med. 2013;20(5):479–486. doi: 10.1111/acem.12134 [DOI] [PubMed] [Google Scholar]
- 3.Vincent GK, Velkoff VA. The Next Four Decades: The Older Population in the United States: 2010 to 2050 Population Estimates and Projections Current Population Reports.; 2010. www.census.gov/population/www. Accessed June 17, 2019.
- 4.Grudzen CR, Richardson LD, Major-Monfried H, Kandarian B, Ortiz JM, Morrison RS. Hospital administrators’ views on barriers and opportunities to delivering palliative care in the emergency department. Ann Emerg Med. 2013;61(6):654–660. doi: 10.1016/j.annemergmed.2012.06.008 [DOI] [PubMed] [Google Scholar]
- 5.Stone SC, Mohanty S, Grudzen CR, et al. Emergency Medicine Physicians’ Perspectives of Providing Palliative Care in an Emergency Department. J Palliat Med. 2011;14(12):1333–1338. doi: 10.1089/jpm.2011.0106 [DOI] [PubMed] [Google Scholar]
- 6.Vasunilashorn S, Steinman BA, Liebig PS, Pynoos J. Aging in Place: Evolution of a Research Topic Whose Time Has Come. J Aging Res. 2012;2012. doi: 10.1155/2012/120952 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Bayer A-H, Harper L. Fixing to Stay: A National Survey on Housing and Home Modification Issues. https://www.aarp.org/home-garden/housing/info-2000/aresearch-import-783.html. Published 2000. Accessed June 18, 2019.
- 8.Teno JM, Gozalo P, Trivedi AN, et al. Site of death, place of care, and health care transitions among US medicare beneficiaries, 2000–2015. In: JAMA - Journal of the American Medical Association. Vol 320. American Medical Association; 2018:264–271. doi: 10.1001/jama.2018.8981 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Flory J, Young-Xu Y, Gurol I, Levinsky N, Ash A, Emanuel E. Place of death: U.S. trends since 1980: Fewer Americans died in the hospital in 1998 than in 1980, but some racial disparities raise troubling questions. Health Aff. 2004;23(3):194–200. doi: 10.1377/hlthaff.23.3.194 [DOI] [PubMed] [Google Scholar]
- 10.Shepperd S, Gonçalves-Bradley DC, Straus SE, Wee B. Hospital at home: Home-based end-of-life care. Cochrane Database Syst Rev. 2016;2016(2). doi: 10.1002/14651858.CD009231.pub2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ewen HH, Washington TR, Emerson KG, Carswell AT, Smith ML. Variation in Older Adult Characteristics by Residence Type and Use of Home- and Community-Based Services. Int J Environ Res Public Health. 2017;14(3). doi: 10.3390/ijerph14030330 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Ryvicker M, Sridharan S. Neighborhood Environment and Disparities in Health Care Access Among Urban Medicare Beneficiaries With Diabetes: A Retrospective Cohort Study. Inquiry. 2018;55:46958018771414. doi: 10.1177/0046958018771414 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Levasseur M, Généreux M, Bruneau J-F, et al. Importance of proximity to resources, social support, transportation and neighborhood security for mobility and social participation in older adults: results from a scoping study. BMC Public Health. 2015;15:503. doi: 10.1186/s12889-015-1824-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Groden SR, Woodward AT, Chatters LM, Taylor RJ. Use of Complementary and Alternative Medicine among Older Adults: Differences between Baby Boomers and Pre-Boomers. Am J Geriatr Psychiatry. 2017;25(12):1393–1401. doi: 10.1016/j.jagp.2017.08.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Leong M, Smith TJ, Rowland-Seymour A. Complementary and integrative medicine for older adults in palliative care. Clin Geriatr Med. 2015;31(2):177–191. doi: 10.1016/j.cger.2015.01.004 [DOI] [PubMed] [Google Scholar]
- 16.Lin YC, Wan L, Jamison RN. Using integrative medicine in pain management: An evaluation of current evidence. Anesth Analg. 2017;125(6):2081–2093. doi: 10.1213/ANE.0000000000002579 [DOI] [PubMed] [Google Scholar]
- 17.Ryder PT, Wolpert B, Orwig D, Carter-Pokras O, Black SA. Complementary and alternative medicine use among older urban African Americans: Individual and neighborhood associations. J Natl Med Assoc. 2008;100(10):1186–1192. doi: 10.1016/S0027-9684(15)31475-9 [DOI] [PubMed] [Google Scholar]
- 18.Sternberg SA, Chandran A, Sikka M. Alternative Therapy Use by Elderly African Americans Attending a Community Clinic. J Am Geriatr Soc. 2003;51(12):1768–1772. doi: 10.1046/j.1532-5415.2003.51562.x [DOI] [PubMed] [Google Scholar]
- 19.AANMC: Licensure. http://aanmc.org/careers/licensure/. Published 2014. Accessed April 25, 2014. [Google Scholar]
- 20.Grudzen CR, Brody AA, Chung FR, et al. Primary Palliative Care for Emergency Medicine (PRIM-ER): Protocol for a Pragmatic, Cluster-Randomised, Stepped Wedge Design to Test the Effectiveness of Primary Palliative Care Education, Training and Technical Support for Emergency Medicine. BMJ Open. 2019;9(7):e030099. doi: 10.1136/bmjopen-2019-030099 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Charlton P, Doucet S, Azar R, et al. The use of the environmental scan in health services delivery research: a scoping review protocol. BMJ Open. 2019;9:29805. doi: 10.1136/bmjopen-2019-029805 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Choo CW. Environmental scanning as information seeking and organizational learning. Inf Res. 2001;7(1). http://informationr.net.libproxy.lib.unc.edu/ir/7-1/paper112.html. Accessed March 16, 2020. [Google Scholar]
- 23.Kozhimannil KB, Hung P, Casey MM, Lorch SA. Factors associated with high-risk rural women giving birth in non-NICU hospital settings. J Perinatol. 2016;36(7):510–515. doi: 10.1038/jp.2016.8 [DOI] [PubMed] [Google Scholar]
- 24.Gresenz CR, Rogowski J, Escarce JJ. Updated Variable-Radius Measures of Hospital Competition. Health Serv Res. 2004;39(2):417–430. doi: 10.1111/j.1475-6773.2004.00235.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Lam O, Brokerick B, Toor S. How far do urban, suburban and rural Americans live from a hospital? | Pew Research Center. Pew Research Center. https://www.pewresearch.org/fact-tank/2018/12/12/how-far-americans-live-from-the-closest-hospital-differs-by-community-type/. Accessed March 16, 2020.
- 26.Smith ML, Prohaska TR, Macleod KE, et al. Non-emergency medical transportation needs of middle-aged and older adults: A rural-urban comparison in delaware, USA. Int J Environ Res Public Health. 2017;14(2). doi: 10.3390/ijerph14020174 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Mattson J Transportation, Distance, and Health Care Utilization for Older Adults in Rural and Small Urban Areas; 2010.
- 28.Census Regions and Divisions of the United States. US Census Bureau. https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf. Accessed January 20, 2020.
- 29.Rothstein’ A, Butler CT, Rothstein A. On Conflict and Consensus: A Handbook on Formal Consensus Decisionmaking.; 2009. www.ic.org. Accessed March 20, 2020.
- 30.Paez A Gray literature: An important resource in systematic reviews. J Evid Based Med. 2017;10(3):233–240. doi: 10.1111/jebm.12266 [DOI] [PubMed] [Google Scholar]
- 31.Home | Dedoose. https://www.dedoose.com/. Accessed January 15, 2020.
- 32.Arbaje AI, Yu Q, Newhall KA, Leff B. Prevalence, Geographic Variation, and Trends in Hospital Services Relevant to the Care of Older Adults: Development of the Senior Care Services Scale and Examination of Measurement Properties. Med Care. 2015;53(9):768–775. doi: 10.1097/MLR.0000000000000408 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Aha. 2018. AHA Annual Survey American Hospital Association.
- 34.Arbaje AI, Yu Q, Wang J, Leff B. Senior services in US hospitals and readmission risk in the Medicare population. Int J Qual Heal Care. 2017;29(6):845–852. doi: 10.1093/intqhc/mzx112 [DOI] [PubMed] [Google Scholar]
- 35.Revenue Center Code (FFS) | ResDAC. https://www.resdac.org/cms-data/variables/revenue-center-code-ffs. Accessed March 23, 2020.
- 36.How to Identify Hospital Claims for Emergency Room Visits in the Medicare Claims Data | ResDAC. https://www.resdac.org/articles/how-identify-hospital-claims-emergency-room-visits-medicare-claims-data. Accessed March 23, 2020.
- 37.Dartmouth Atlas Data. https://atlasdata.dartmouth.edu/. Accessed January 27, 2020.
- 38.Barnato AE, Farrell MH, Chang CCH, Lave JR, Roberts MS, Angus DC. Development and validation of hospital end-of-life treatment intensity measures. Med Care. 2009;47(10):1098–1105. doi: 10.1097/MLR.0b013e3181993191 [DOI] [PMC free article] [PubMed] [Google Scholar]
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