Critical Care in the Military Health System: A Survey-Based Summary of Critical Care Services

Critical Care in the Military Health System: A Survey-Based Summary of Critical Care Services Abstract Introduction Critical care is an important component of in-patient and combat casualty care, and it is a major contributor to U.S. healthcare costs. Regular exposure to critically ill and injured patients may directly contribute to wartime skills retention for military caregivers. Data describing critical care services in the Military Health System (MHS), however, is lacking. This study was undertaken to describe MHS critical care services, their resource utilization, and differences in care practices amongst military treatment facilities (MTFs). Materials and Methods Twenty-six MTFs representing 38 adult critical care services or intensive care units (ICUs) were surveyed. The survey collected information about organizational structure, resourcing, and unit characteristics at the time of a concurrent 24-h point-prevalence survey designed to describe patient characteristics and staffing in these facilities. The survey was anonymous and protected health information was not collected. We analyzed the data according to high capacity centers (HCCs) (≥200 beds) and low capacity centers (LCCs) (<200 beds). Differences between HCCs and LCCs were compared using Fisher’s exact test. Results Seventeen MTFs (7 HCCs and 10 LCCs), representing 27 ICUs, responded to the survey. This was a 65% response rate for MTFs and a 71% response rate for services/ICUs. HCCs reported more closed vs. open ICUs; more dedicated critical care services (i.e., medical and surgical ICUs vs. mixed ICUs); fewer respiratory therapists available, but more with certification; more total nursing staff and more critical care certified nurses; the use of subjectively more effective protocols (10.5 vs. 6.7 protocols/unit or service); higher utilization of an ICU daily rounds checklist (65% vs. 0%); and less consistency of clinician type participation during multidisciplinary rounds. ICU leadership structure was similar among the institutions. The majority of respondents were unable to provide summary APACHE II scores, but HCCs were more likely to submit this information than LCCs. Most centers perform multidisciplinary rounds daily, but they are more likely to be run by a physician credentialed in critical care at HCCs (85% vs. 59%, p < 0.05). 67% of respondents reported mortality rates <5%. The two services that reported mortality rates greater than 10% were both LCCs. Conclusion This is the first comprehensive report about MHS critical care services. Despite notable variability in data reporting, an important finding itself, this study highlights notable differences in organizational structure and resourcing between HCCs and LCCs within the MHS. The clinical implication of these differences (i.e., impact on patient outcomes) of these differences require further study. Better understanding of MHS critical care services may improve enterprise decision-making about these services which could ultimately improve care of combat casualties. INTRODUCTION In the USA, over 5 million patients are admitted annually to the intensive care unit (ICU), which constitutes one of the largest portions of national healthcare expenditures. These costs increased from $56.6 to $81.7 billion from 2000 to 2005 and are predicted to continue to rise as the number of patients over age 65 will double between 2007 and 2030.1,2 ICU admission and subsequent critical care represent approximately 13% of all hospital costs, 4% of national health expenditures, and 1% of the U.S. gross domestic product.3,4 According to Halpern, each day of ICU care costs on average $3500 per patient.1 Considering the high volume of critically ill patients with increased complexity, and the significant cost in providing this care, it is imperative that organizations understand critical care services in order to streamline care processes, minimize costs, and ultimately optimize patient safety and outcome. There are 26 military treatment facilities (MTFs) with approximately 300–400 total ICU beds that provide critical care services in the Military Health System (MHS). Unfortunately, little is known about these services as past efforts to accurately describe them have failed.6 There are no dedicated databases that describe critical care services for the enterprise and local MTF data are difficult to identify. There is also no centralized or enterprise level leadership or organizational structure specific to critical care: instead, critical care leadership and services are fractured among more traditional organizational lines of accountability: surgery, medicine, anesthesia, and nursing. This fractured approach to critical care for the MHS makes it difficult to identify and collect important data that might influence how the military delivers this important service to its beneficiaries and warfighter. The primary objective of this observational study was to produce an accurate snapshot of current critical care services, capacity, resources, quality indicators, and critical care outcomes for critical care leaders across the MHS. METHODS We performed an anonymous study of 26 MTFs (15 Army, 7 Air Force, and 4 Navy) representing a total of 38 adult critical care services or physical ICUs. The study consisted of two parts: an administrative survey and a point-prevalence study of all patients admitted to the unit/service during a 24-h period. The study was submitted to the U.S. Army Institute of Surgical Research’s Regulatory Office and determined to be research not involving human subjects. This manuscript reports the results of the administrative survey portion of the study only. The results of the point-prevalence study are reported separately in this journal. The administrative survey was designed to help identify the major differences between institutions with respect to care models, patient populations, and management practices and was modeled after similar surveys created by the United States Critical Illness and Injuries Trials Group Critical Illness Outcomes Study (USCIITG-CIOS) and the New York Hospital Association Critical Care Leaders Network (NYHA CCLN).6,7 The survey collected data about organizational structure/resourcing, unit characteristics, patient population, and the availability and perceived effectiveness of critical care protocols, (see Supplementary Material for the survey included with the online content for this manuscript). Prior to administration, the survey underwent a process for face validation and questions that were not valid were revised. Points-of-contact involved in critical care at each facility completed the survey at any time during the month prior to or the month following the 24-h prevalence study. We asked all MHS MTFs that have designated critical care beds to participate in this survey. At the beginning of the study, the principal investigator (PI, author JCP) contacted each MTF to identify ICU leadership and their contact information. An e-mail was sent to these points-of-contact (POCs) with a request to participate. Instructions for completing the survey were also included. In order to determine how many centers participated, we asked identified POCs at each center to declare by e-mail if their center participated, but individual center survey data were kept anonymous. To maintain anonymity of data, POCs created a self-identified code name, known only by the center POCs, to be used in the survey in order to aggregate data for a single facility if that facility reported from multiple critical care services or separate ICUs. In other words, large centers could have each ICU service (i.e., medical, surgical, trauma critical care service) or unit (i.e., medical ICU, surgical ICU, etc.) respond to the survey, but each service/unit identified itself with the site-specific code name. For the purposes of the survey, a service was defined as a dedicated physician team whereas a unit was a physical location that may or may not be dedicated to a specific patient population. A master list of physical sites and their associated code names was not created and POC information was not collected in the survey. Thus, only POCs from a facility could identify which data belonged to that facility. The surveys were completed by POCs or POC designee(s) at each MTF. This POC was usually a medical director or a physician/nurse leader identified by the PI through networking with key leaders in critical care throughout the MHS. POCs worked with leaders of other multidisciplinary groups to identify the most reliable source of information for the survey. POCs personally vouched for the accuracy of the data. A list of POCs and his/her facility is in the Acknowledgements section. In situations where the requested information was not available, POCs could answer as such. We analyzed the data according to high capacity centers (HCCs) and low capacity centers (LCCs). HCCs were defined as having greater than 200 beds, whereas LCCs had less than or equal to 200 beds. This cutoff was chosen to separate the larger, academic referral medical centers from the smaller MTFs that more closely represent community hospitals. We further described centers in terms of service/unit types, care models, resources, protocol availability, and population data/unit demographics. Differences between HCCs and LCCs were compared using Fisher’s exact test with significance set at p < 0.05. RESULTS Seventeen (65% response rate) of MTFs responded to the survey representing 27 ICUs (71% response rate) across the MHS. Respondents from HCCs more often reported on behalf of a service, whereas respondents from LCCs more often reported as a physical unit. HCCs more often reported having multiple “owners,” or stakeholders (median of 2; examples include the Department of Medicine, the Department of Surgery, Pulmonary Critical Care Service, Anesthesia and Operative Services, etc.), of their critical care services than LCCs (median of 1). The median annual admissions for a HCC were 500–1000 patients; whereas, the median annual admissions for LCCs were 400–500 with three locations reporting no data (Table I). Table I. Reporting Characteristics of MHS Critical Care Services Unit/Service Reporting Characteristics Label Bedsa Physical ICUs # Services “Owners” of CCa Annual Admissionsa Average Daily Censusa Open vs. Closed Admitting Crit Care Consultation Average APACHE Score (Admission)a HC-1 201–300 3 2 2 1000–2000 6–10 Open By Criteria 11–15 HC-2 201–300 3 4 2 100–1000 6–10 Mixed By Criteria 11–15 HC-3 201–300 2 3 2 500–1000 3–5 Closed Closed UNK HC-4 401–500 8 6 3–7 100–2000 6–15 4C, 2O Closed or All 21–25; 5 x UNK Medians 200–300 3 4 2 500–1000 6–10 46% C 42% Closed 54% UNK LC-1 <50 1 2 1 500–1000 1–2 Open By Criteria 36–40 LC-2 <50 1 3 1 300–400 1–2 Mixed All 6–10 LC-3 50–100 1 0 1 500–1000 3–5 Open None UNK LC-4 50–100 1 0 3 300–400 1–2 Open None 6–10 LC-5 50–100 1 1 1 500–1000 3–5 Open All UNK LC-6 50–100 1 3 1 500–1000 6–10 Open By Criteria UNK LC-7 50–100 1 1 2 200–300 1–2 Open By Criteria UNK LC-8 50–100 1 2 1 50–100 1–2 Open By Criteria 6–10 LC-9 50–100 1 3 1 UNK 6–10 Mixed None UNK LC-10 50–100 1 0 3 400–500 3–5 Open None UNK LC-11 50–100 0 0 1 UNK 3–5 Open None UNK LC-12 101–200 1 1 1 300–400 3–5 Open Svc Dependent UNK LC-13 101–200 1 1 1 UNK 3–5 Closed All UNK LC-14 101–200 1 1 1 400–500 3–5 Closed Closed UNK Medians 50–100 1 1 1 400–500(3xUNK) 3–5 14% C 36% None 71% UNK Unit/Service Reporting Characteristics Label Bedsa Physical ICUs # Services “Owners” of CCa Annual Admissionsa Average Daily Censusa Open vs. Closed Admitting Crit Care Consultation Average APACHE Score (Admission)a HC-1 201–300 3 2 2 1000–2000 6–10 Open By Criteria 11–15 HC-2 201–300 3 4 2 100–1000 6–10 Mixed By Criteria 11–15 HC-3 201–300 2 3 2 500–1000 3–5 Closed Closed UNK HC-4 401–500 8 6 3–7 100–2000 6–15 4C, 2O Closed or All 21–25; 5 x UNK Medians 200–300 3 4 2 500–1000 6–10 46% C 42% Closed 54% UNK LC-1 <50 1 2 1 500–1000 1–2 Open By Criteria 36–40 LC-2 <50 1 3 1 300–400 1–2 Mixed All 6–10 LC-3 50–100 1 0 1 500–1000 3–5 Open None UNK LC-4 50–100 1 0 3 300–400 1–2 Open None 6–10 LC-5 50–100 1 1 1 500–1000 3–5 Open All UNK LC-6 50–100 1 3 1 500–1000 6–10 Open By Criteria UNK LC-7 50–100 1 1 2 200–300 1–2 Open By Criteria UNK LC-8 50–100 1 2 1 50–100 1–2 Open By Criteria 6–10 LC-9 50–100 1 3 1 UNK 6–10 Mixed None UNK LC-10 50–100 1 0 3 400–500 3–5 Open None UNK LC-11 50–100 0 0 1 UNK 3–5 Open None UNK LC-12 101–200 1 1 1 300–400 3–5 Open Svc Dependent UNK LC-13 101–200 1 1 1 UNK 3–5 Closed All UNK LC-14 101–200 1 1 1 400–500 3–5 Closed Closed UNK Medians 50–100 1 1 1 400–500(3xUNK) 3–5 14% C 36% None 71% UNK Examples of “owners” include the Department of Medicine, the Department of Surgery, Pulmonary Critical Care Service, Anesthesia and Operative Services, etc. Average daily census is for each service or physical unit in HCCs and for the physical ICU in the LCCs. Thus, total census for HC-3 with two physical ICUs and three services would be 6–15. C, Closed; CC, critical care; O, open; UNK, unknown. aRanges in this table indicate all responses provided to the survey. In some cases, multiple services within one organization responded yielding wide variation in response. For example, HC-4 indicated that one service only had 100 admissions per year whereas another had up to 2000. Response options can be reviewed in the supplementary material. Table I. Reporting Characteristics of MHS Critical Care Services Unit/Service Reporting Characteristics Label Bedsa Physical ICUs # Services “Owners” of CCa Annual Admissionsa Average Daily Censusa Open vs. Closed Admitting Crit Care Consultation Average APACHE Score (Admission)a HC-1 201–300 3 2 2 1000–2000 6–10 Open By Criteria 11–15 HC-2 201–300 3 4 2 100–1000 6–10 Mixed By Criteria 11–15 HC-3 201–300 2 3 2 500–1000 3–5 Closed Closed UNK HC-4 401–500 8 6 3–7 100–2000 6–15 4C, 2O Closed or All 21–25; 5 x UNK Medians 200–300 3 4 2 500–1000 6–10 46% C 42% Closed 54% UNK LC-1 <50 1 2 1 500–1000 1–2 Open By Criteria 36–40 LC-2 <50 1 3 1 300–400 1–2 Mixed All 6–10 LC-3 50–100 1 0 1 500–1000 3–5 Open None UNK LC-4 50–100 1 0 3 300–400 1–2 Open None 6–10 LC-5 50–100 1 1 1 500–1000 3–5 Open All UNK LC-6 50–100 1 3 1 500–1000 6–10 Open By Criteria UNK LC-7 50–100 1 1 2 200–300 1–2 Open By Criteria UNK LC-8 50–100 1 2 1 50–100 1–2 Open By Criteria 6–10 LC-9 50–100 1 3 1 UNK 6–10 Mixed None UNK LC-10 50–100 1 0 3 400–500 3–5 Open None UNK LC-11 50–100 0 0 1 UNK 3–5 Open None UNK LC-12 101–200 1 1 1 300–400 3–5 Open Svc Dependent UNK LC-13 101–200 1 1 1 UNK 3–5 Closed All UNK LC-14 101–200 1 1 1 400–500 3–5 Closed Closed UNK Medians 50–100 1 1 1 400–500(3xUNK) 3–5 14% C 36% None 71% UNK Unit/Service Reporting Characteristics Label Bedsa Physical ICUs # Services “Owners” of CCa Annual Admissionsa Average Daily Censusa Open vs. Closed Admitting Crit Care Consultation Average APACHE Score (Admission)a HC-1 201–300 3 2 2 1000–2000 6–10 Open By Criteria 11–15 HC-2 201–300 3 4 2 100–1000 6–10 Mixed By Criteria 11–15 HC-3 201–300 2 3 2 500–1000 3–5 Closed Closed UNK HC-4 401–500 8 6 3–7 100–2000 6–15 4C, 2O Closed or All 21–25; 5 x UNK Medians 200–300 3 4 2 500–1000 6–10 46% C 42% Closed 54% UNK LC-1 <50 1 2 1 500–1000 1–2 Open By Criteria 36–40 LC-2 <50 1 3 1 300–400 1–2 Mixed All 6–10 LC-3 50–100 1 0 1 500–1000 3–5 Open None UNK LC-4 50–100 1 0 3 300–400 1–2 Open None 6–10 LC-5 50–100 1 1 1 500–1000 3–5 Open All UNK LC-6 50–100 1 3 1 500–1000 6–10 Open By Criteria UNK LC-7 50–100 1 1 2 200–300 1–2 Open By Criteria UNK LC-8 50–100 1 2 1 50–100 1–2 Open By Criteria 6–10 LC-9 50–100 1 3 1 UNK 6–10 Mixed None UNK LC-10 50–100 1 0 3 400–500 3–5 Open None UNK LC-11 50–100 0 0 1 UNK 3–5 Open None UNK LC-12 101–200 1 1 1 300–400 3–5 Open Svc Dependent UNK LC-13 101–200 1 1 1 UNK 3–5 Closed All UNK LC-14 101–200 1 1 1 400–500 3–5 Closed Closed UNK Medians 50–100 1 1 1 400–500(3xUNK) 3–5 14% C 36% None 71% UNK Examples of “owners” include the Department of Medicine, the Department of Surgery, Pulmonary Critical Care Service, Anesthesia and Operative Services, etc. Average daily census is for each service or physical unit in HCCs and for the physical ICU in the LCCs. Thus, total census for HC-3 with two physical ICUs and three services would be 6–15. C, Closed; CC, critical care; O, open; UNK, unknown. aRanges in this table indicate all responses provided to the survey. In some cases, multiple services within one organization responded yielding wide variation in response. For example, HC-4 indicated that one service only had 100 admissions per year whereas another had up to 2000. Response options can be reviewed in the supplementary material. The results show a wide variation in practice and patient census among critical care services in the MHS. HCCs reported more ICUs dedicated to a specific patient population (e.g., medical or surgical) whereas LCCs reported more mixed population ICUs. HCCs reported more closed vs. open ICUs as a care model, more access to critical care trained physicians (100% vs. 64%), and more dedicated critical care services (i.e., Medical and Surgical ICUs vs. Mixed ICUs) (Table I). The majority of respondents were unable to provide APACHE II scores making it difficult to describe the complexity of critical illness cared for by MHS critical care services over the last year. Only 30% of HCCs and 28% of LCC were able to report average APACHE II scores for the past year. Of the LCCs that reported APACHE II scores, there were three centers that reported median ranges of 6–10 (approximate 8% mortality); one LCC reported a median APACHE II range of 36–40 (approximate 75% mortality). Of the HCCs services that reported, two services reported an APACHE II range of 11–15, and one service reported a range of 21–25 (Table I). HCCs reported fewer respiratory therapists per service but more with registered respiratory therapist (RRT) certification. HCCs also reported more nursing staff and slightly more nurses with the critical care registered nursing (CCRN) certification. LCCs reported more nursing experience by median range for years of experience (Table II). The presence of the multidisciplinary care team during multidisciplinary rounds (MDR) differed between HCCs and LCCs. Nurses are more frequently present during MDR at LCCs (86% vs. 54%), as are ancillary or specialty support staff. There are significant differences in the availability of dietitians (64% vs. 0%, p < 0.05), pharmacists (71% vs. 0%, p < 0.05), and case manager/social workers (36% vs. 0%, p < 0.05) in LCCs vs. HCCs. However, there is a wider range of ancillary or specialty support type in the HCCs (Table III). For example, HCCs reported more presence of medical students, resident physicians, and mid-level providers than did LCCs. Table II. Staffing Resources Across the MHS Critical Care Services Staffing Location Nurses on Staff % Nurses CCRN Certified Average Nursing Experience RTs Available to the ICU % RTs RRT Certified HCC-1 41–50 51–60 6–10 1–2 91–99 HCC-2 41–50 31–40 1–5 6–10 71–80 HCC-3 41–60 41–50 1–5 1–2 100 HCC-4 31 to >80 21–70 6–10 3–20 <5 to 80 Median 41–50 41–60 1–10 Variable Variable LCC-1 12 <5 6–10 10–15 51–60 LCC-2 6–10 91–99 1–5 6–10 51–60 LCC-3 31–40 UNK 11–20 6–10 100 LCC-4 16–20 21–30 6–10 3–5 71–80 LCC-5 31–40 41–50 11–20 16–20 41–50 LCC-6 16–20 81–90 6–10 6–10 81–90 LCC-7 11–15 41–50 11–20 10–15 11–20 LCC-8 11–15 41–50 6–10 16–20 5–10 LCC-9 16–20 41–50 1–5 1–2 41–50 LCC-10 16–20 41–50 6–10 10–15 <5 LCC-11 11–15 61–70 6–10 6–10 41–50 LCC-12 21–30 81–90 UNK 6–10 UNK LCC-13 3–5 81–90 1–5 1–2 100 LCC-14 21–30 61–70 6–10 16–20 61–70 Median 16–20 41–50 (4 > 80%) 6–10 6–10 51–60 Staffing Location Nurses on Staff % Nurses CCRN Certified Average Nursing Experience RTs Available to the ICU % RTs RRT Certified HCC-1 41–50 51–60 6–10 1–2 91–99 HCC-2 41–50 31–40 1–5 6–10 71–80 HCC-3 41–60 41–50 1–5 1–2 100 HCC-4 31 to >80 21–70 6–10 3–20 <5 to 80 Median 41–50 41–60 1–10 Variable Variable LCC-1 12 <5 6–10 10–15 51–60 LCC-2 6–10 91–99 1–5 6–10 51–60 LCC-3 31–40 UNK 11–20 6–10 100 LCC-4 16–20 21–30 6–10 3–5 71–80 LCC-5 31–40 41–50 11–20 16–20 41–50 LCC-6 16–20 81–90 6–10 6–10 81–90 LCC-7 11–15 41–50 11–20 10–15 11–20 LCC-8 11–15 41–50 6–10 16–20 5–10 LCC-9 16–20 41–50 1–5 1–2 41–50 LCC-10 16–20 41–50 6–10 10–15 <5 LCC-11 11–15 61–70 6–10 6–10 41–50 LCC-12 21–30 81–90 UNK 6–10 UNK LCC-13 3–5 81–90 1–5 1–2 100 LCC-14 21–30 61–70 6–10 16–20 61–70 Median 16–20 41–50 (4 > 80%) 6–10 6–10 51–60 UNK, unknown. Table II. Staffing Resources Across the MHS Critical Care Services Staffing Location Nurses on Staff % Nurses CCRN Certified Average Nursing Experience RTs Available to the ICU % RTs RRT Certified HCC-1 41–50 51–60 6–10 1–2 91–99 HCC-2 41–50 31–40 1–5 6–10 71–80 HCC-3 41–60 41–50 1–5 1–2 100 HCC-4 31 to >80 21–70 6–10 3–20 <5 to 80 Median 41–50 41–60 1–10 Variable Variable LCC-1 12 <5 6–10 10–15 51–60 LCC-2 6–10 91–99 1–5 6–10 51–60 LCC-3 31–40 UNK 11–20 6–10 100 LCC-4 16–20 21–30 6–10 3–5 71–80 LCC-5 31–40 41–50 11–20 16–20 41–50 LCC-6 16–20 81–90 6–10 6–10 81–90 LCC-7 11–15 41–50 11–20 10–15 11–20 LCC-8 11–15 41–50 6–10 16–20 5–10 LCC-9 16–20 41–50 1–5 1–2 41–50 LCC-10 16–20 41–50 6–10 10–15 <5 LCC-11 11–15 61–70 6–10 6–10 41–50 LCC-12 21–30 81–90 UNK 6–10 UNK LCC-13 3–5 81–90 1–5 1–2 100 LCC-14 21–30 61–70 6–10 16–20 61–70 Median 16–20 41–50 (4 > 80%) 6–10 6–10 51–60 Staffing Location Nurses on Staff % Nurses CCRN Certified Average Nursing Experience RTs Available to the ICU % RTs RRT Certified HCC-1 41–50 51–60 6–10 1–2 91–99 HCC-2 41–50 31–40 1–5 6–10 71–80 HCC-3 41–60 41–50 1–5 1–2 100 HCC-4 31 to >80 21–70 6–10 3–20 <5 to 80 Median 41–50 41–60 1–10 Variable Variable LCC-1 12 <5 6–10 10–15 51–60 LCC-2 6–10 91–99 1–5 6–10 51–60 LCC-3 31–40 UNK 11–20 6–10 100 LCC-4 16–20 21–30 6–10 3–5 71–80 LCC-5 31–40 41–50 11–20 16–20 41–50 LCC-6 16–20 81–90 6–10 6–10 81–90 LCC-7 11–15 41–50 11–20 10–15 11–20 LCC-8 11–15 41–50 6–10 16–20 5–10 LCC-9 16–20 41–50 1–5 1–2 41–50 LCC-10 16–20 41–50 6–10 10–15 <5 LCC-11 11–15 61–70 6–10 6–10 41–50 LCC-12 21–30 81–90 UNK 6–10 UNK LCC-13 3–5 81–90 1–5 1–2 100 LCC-14 21–30 61–70 6–10 16–20 61–70 Median 16–20 41–50 (4 > 80%) 6–10 6–10 51–60 UNK, unknown. Table III. The Frequency with Which Specified Clinician Types Are Present During Multidisciplinary Rounds in the ICU Clinicians Present on MDR Team Member Low Capacity High Capacity (Always %/Total %) (Always %/Total %) Attending/staff physician 100/100 100/100 Bedside Nurse 86/100 54/100 Dietician* 64/100 0/54 Pharmacist* 71/100 0/62 Charge nurse 57/86 15/54 Case manager or social worker* 36/93 0/38 Student 0/86 8/62 Resident physician (physician in training) 50/79 77/92 Respiratory therapist 29/86 15/46 Clinical nurse specialist 0/57 0/69 Head nurse 7/57 0/54 Physician extender (mid-level provider) 7/29 15/46 Physical therapist 7/29 8/8 Psychologist 7/14 0/0 Clinicians Present on MDR Team Member Low Capacity High Capacity (Always %/Total %) (Always %/Total %) Attending/staff physician 100/100 100/100 Bedside Nurse 86/100 54/100 Dietician* 64/100 0/54 Pharmacist* 71/100 0/62 Charge nurse 57/86 15/54 Case manager or social worker* 36/93 0/38 Student 0/86 8/62 Resident physician (physician in training) 50/79 77/92 Respiratory therapist 29/86 15/46 Clinical nurse specialist 0/57 0/69 Head nurse 7/57 0/54 Physician extender (mid-level provider) 7/29 15/46 Physical therapist 7/29 8/8 Psychologist 7/14 0/0 *p < 0.05 for comparison of protocol presence at HCCs vs. LCCs. Table III. The Frequency with Which Specified Clinician Types Are Present During Multidisciplinary Rounds in the ICU Clinicians Present on MDR Team Member Low Capacity High Capacity (Always %/Total %) (Always %/Total %) Attending/staff physician 100/100 100/100 Bedside Nurse 86/100 54/100 Dietician* 64/100 0/54 Pharmacist* 71/100 0/62 Charge nurse 57/86 15/54 Case manager or social worker* 36/93 0/38 Student 0/86 8/62 Resident physician (physician in training) 50/79 77/92 Respiratory therapist 29/86 15/46 Clinical nurse specialist 0/57 0/69 Head nurse 7/57 0/54 Physician extender (mid-level provider) 7/29 15/46 Physical therapist 7/29 8/8 Psychologist 7/14 0/0 Clinicians Present on MDR Team Member Low Capacity High Capacity (Always %/Total %) (Always %/Total %) Attending/staff physician 100/100 100/100 Bedside Nurse 86/100 54/100 Dietician* 64/100 0/54 Pharmacist* 71/100 0/62 Charge nurse 57/86 15/54 Case manager or social worker* 36/93 0/38 Student 0/86 8/62 Resident physician (physician in training) 50/79 77/92 Respiratory therapist 29/86 15/46 Clinical nurse specialist 0/57 0/69 Head nurse 7/57 0/54 Physician extender (mid-level provider) 7/29 15/46 Physical therapist 7/29 8/8 Psychologist 7/14 0/0 *p < 0.05 for comparison of protocol presence at HCCs vs. LCCs. Within the MHS, most centers perform MDR daily (HCCs 92% vs. LCCs 79%). However, these are more likely to be run by a physician certified in critical care at HCCs (85% vs. 59%, p < 0.05). MDR are less likely to occur on the weekends than during weekdays (HCCs 69% vs. LCCs 64%). HCCs are more likely to use checklists during MDR than LCCs (85% vs. 29%, p < 0.05), although in both it is attending physician dependent (HCCs 62% vs. LCCs 36%). HCCs had twice as many protocols as LCCs. When asked if protocols were present and effective/successful, HCCs reported more protocols than LCCs (10.5 vs. 6.7 protocols/unit or service). HCCs seemed to subjectively find more of their protocols to be effective as well; HCCs reported 62% of protocols to be effective vs. LCCs reporting 32% (p < 0.05) of protocols to be effective (Table IV). Table IV. Number of Respondents from HCCs and LCCs that Reported Present, Effective and Successful ICU Protocols vs. the Number Without the Protocol or with One Present that Does Not Effectively Achieve Its Goals Critical Care Protocols HCCs (n = 13) LCCs (n = 14) Present, Effective, Successful Absent or Ineffective Present, Effective, Successful Absent or Ineffective Unknown or Blank Response n (%) n (%) n (%) n (%) n (%) Standard ventilator management 3 (23) 10 (77) 4 (29) 8 (57) 2 Ventilator liberation 11 (85) 2 (15) 7 (50) 7 (50) 0 Respiratory adjunctive therapies 9 (69) 4 (31) 4 (29) 9 (64) 1 Sedation 10 (77) 3 (23) 8 (57) 6 (43) 0 Neuro-muscular paralysis 4 (31) 9 (69) 2 (14) 11 (79) 1 Sleep 5 (38) 8 (62) 3 (21) 11 (79) 0 Delirium management 5 (38) 8 (62) 6 (43) 8 (57) 0 Tight glycemic control* 13 (100) 0 (0) 4 (29) 8 (57) 2 Mobility* 11 (85) 2 (15) 1 (7) 13 (93) 0 Nutrition* 11 (85) 2 (15) 5 (36) 9 (64) 0 DVT prophylaxis 11 (85) 2 (15) 9 (64) 5 (36) 0 GI prophylaxis 9 (69) 4 (31) 8 (57) 6 (43) 0 Therapeutic hypothermia* 12 (92) 1 (8) 6 (43) 8 (57) 0 Euthermia* 7 (54) 6 (46) 1 (7) 12 (86) 1 TBI or brain injury management 6 (46) 7 (54) 0 (0) 14 (100) 0 Massive transfusion* 12 (92) 1 (8) 6 (43) 7 (50) 1 Sepsis 3 (23) 10 (77) 4 (29) 10 (71) 0 ECMO/ECLS* 4 (31) 9 (69) 0 (0) 14 (100) 0 TPE 3 (23) 10 (77) 0 (0) 14 (100) 0 Total 149 (60) 98 (40) 78 (29) 180 (68) 8 (3) Critical Care Protocols HCCs (n = 13) LCCs (n = 14) Present, Effective, Successful Absent or Ineffective Present, Effective, Successful Absent or Ineffective Unknown or Blank Response n (%) n (%) n (%) n (%) n (%) Standard ventilator management 3 (23) 10 (77) 4 (29) 8 (57) 2 Ventilator liberation 11 (85) 2 (15) 7 (50) 7 (50) 0 Respiratory adjunctive therapies 9 (69) 4 (31) 4 (29) 9 (64) 1 Sedation 10 (77) 3 (23) 8 (57) 6 (43) 0 Neuro-muscular paralysis 4 (31) 9 (69) 2 (14) 11 (79) 1 Sleep 5 (38) 8 (62) 3 (21) 11 (79) 0 Delirium management 5 (38) 8 (62) 6 (43) 8 (57) 0 Tight glycemic control* 13 (100) 0 (0) 4 (29) 8 (57) 2 Mobility* 11 (85) 2 (15) 1 (7) 13 (93) 0 Nutrition* 11 (85) 2 (15) 5 (36) 9 (64) 0 DVT prophylaxis 11 (85) 2 (15) 9 (64) 5 (36) 0 GI prophylaxis 9 (69) 4 (31) 8 (57) 6 (43) 0 Therapeutic hypothermia* 12 (92) 1 (8) 6 (43) 8 (57) 0 Euthermia* 7 (54) 6 (46) 1 (7) 12 (86) 1 TBI or brain injury management 6 (46) 7 (54) 0 (0) 14 (100) 0 Massive transfusion* 12 (92) 1 (8) 6 (43) 7 (50) 1 Sepsis 3 (23) 10 (77) 4 (29) 10 (71) 0 ECMO/ECLS* 4 (31) 9 (69) 0 (0) 14 (100) 0 TPE 3 (23) 10 (77) 0 (0) 14 (100) 0 Total 149 (60) 98 (40) 78 (29) 180 (68) 8 (3) Bold and * indicates p < 0.05 for comparison of protocol presence at HCCS vs. LCCs. DVT, deep vein thrombosis; ECLS, extracorporeal life support; ECMO, extracorporeal membrane oxygenation; TBI, traumatic brain injury; GI, gastrointestinal; TPE, therapeutic plasma exchange. Table IV. Number of Respondents from HCCs and LCCs that Reported Present, Effective and Successful ICU Protocols vs. the Number Without the Protocol or with One Present that Does Not Effectively Achieve Its Goals Critical Care Protocols HCCs (n = 13) LCCs (n = 14) Present, Effective, Successful Absent or Ineffective Present, Effective, Successful Absent or Ineffective Unknown or Blank Response n (%) n (%) n (%) n (%) n (%) Standard ventilator management 3 (23) 10 (77) 4 (29) 8 (57) 2 Ventilator liberation 11 (85) 2 (15) 7 (50) 7 (50) 0 Respiratory adjunctive therapies 9 (69) 4 (31) 4 (29) 9 (64) 1 Sedation 10 (77) 3 (23) 8 (57) 6 (43) 0 Neuro-muscular paralysis 4 (31) 9 (69) 2 (14) 11 (79) 1 Sleep 5 (38) 8 (62) 3 (21) 11 (79) 0 Delirium management 5 (38) 8 (62) 6 (43) 8 (57) 0 Tight glycemic control* 13 (100) 0 (0) 4 (29) 8 (57) 2 Mobility* 11 (85) 2 (15) 1 (7) 13 (93) 0 Nutrition* 11 (85) 2 (15) 5 (36) 9 (64) 0 DVT prophylaxis 11 (85) 2 (15) 9 (64) 5 (36) 0 GI prophylaxis 9 (69) 4 (31) 8 (57) 6 (43) 0 Therapeutic hypothermia* 12 (92) 1 (8) 6 (43) 8 (57) 0 Euthermia* 7 (54) 6 (46) 1 (7) 12 (86) 1 TBI or brain injury management 6 (46) 7 (54) 0 (0) 14 (100) 0 Massive transfusion* 12 (92) 1 (8) 6 (43) 7 (50) 1 Sepsis 3 (23) 10 (77) 4 (29) 10 (71) 0 ECMO/ECLS* 4 (31) 9 (69) 0 (0) 14 (100) 0 TPE 3 (23) 10 (77) 0 (0) 14 (100) 0 Total 149 (60) 98 (40) 78 (29) 180 (68) 8 (3) Critical Care Protocols HCCs (n = 13) LCCs (n = 14) Present, Effective, Successful Absent or Ineffective Present, Effective, Successful Absent or Ineffective Unknown or Blank Response n (%) n (%) n (%) n (%) n (%) Standard ventilator management 3 (23) 10 (77) 4 (29) 8 (57) 2 Ventilator liberation 11 (85) 2 (15) 7 (50) 7 (50) 0 Respiratory adjunctive therapies 9 (69) 4 (31) 4 (29) 9 (64) 1 Sedation 10 (77) 3 (23) 8 (57) 6 (43) 0 Neuro-muscular paralysis 4 (31) 9 (69) 2 (14) 11 (79) 1 Sleep 5 (38) 8 (62) 3 (21) 11 (79) 0 Delirium management 5 (38) 8 (62) 6 (43) 8 (57) 0 Tight glycemic control* 13 (100) 0 (0) 4 (29) 8 (57) 2 Mobility* 11 (85) 2 (15) 1 (7) 13 (93) 0 Nutrition* 11 (85) 2 (15) 5 (36) 9 (64) 0 DVT prophylaxis 11 (85) 2 (15) 9 (64) 5 (36) 0 GI prophylaxis 9 (69) 4 (31) 8 (57) 6 (43) 0 Therapeutic hypothermia* 12 (92) 1 (8) 6 (43) 8 (57) 0 Euthermia* 7 (54) 6 (46) 1 (7) 12 (86) 1 TBI or brain injury management 6 (46) 7 (54) 0 (0) 14 (100) 0 Massive transfusion* 12 (92) 1 (8) 6 (43) 7 (50) 1 Sepsis 3 (23) 10 (77) 4 (29) 10 (71) 0 ECMO/ECLS* 4 (31) 9 (69) 0 (0) 14 (100) 0 TPE 3 (23) 10 (77) 0 (0) 14 (100) 0 Total 149 (60) 98 (40) 78 (29) 180 (68) 8 (3) Bold and * indicates p < 0.05 for comparison of protocol presence at HCCS vs. LCCs. DVT, deep vein thrombosis; ECLS, extracorporeal life support; ECMO, extracorporeal membrane oxygenation; TBI, traumatic brain injury; GI, gastrointestinal; TPE, therapeutic plasma exchange. Overall, ICU mortality rates across the MHS are low. About 67% of respondents/services reported mortality rates <5% per year, 11% (three respondents) reported rates of 5–10%, one respondent reported a rate of 11–15%, and one reported a rate of 16–20% (Table V). The two services that reported the highest mortality rates were both LCCs with mixed ICUs. Other outcomes such as ICU length of stay (LOS, 3–4 d), ventilator-associated pneumonia (<1/1000) and central line associated blood stream infection (CLABSI, <1/1000) rates were reported as being very low and did not differ between HCCs and LCCs, although one HCC service reported a higher CLABSI rate of 16–20/1000 over the past year. We investigated how critical care services are organized and structured in the MHS. All of LCC and HCC respondents have a physician medical director and head nurse. Seventy-seven percent of HCCs vs. 50% of LCCs reported regularly scheduled meetings between the medical director and the head nurse. Hospital Critical Care Committees were reported as active at all HCCs and only 71% of LCCs. We investigated how often MHS critical care services transfer patients to a higher level of care. During the 3 mo before the survey, HCCs compared with LCCs transferred notably fewer total patients to higher levels of civilian care (3 vs. 58) and military care (0 vs. 9). DISCUSSION Within the MHS, critical care services clearly differ between HCCs and LCCs. HCCs have more service specific ICUs and were more likely to report on behalf of a service rather than a unit or physical location. Structure and process of care are important determinants of patient outcome in the ICU.8 Although controversy exists regarding the benefits of closed vs. open critical care delivery models,8 military researchers have suggested that intensivist-led, high performing, multidisciplinary critical care teams improve outcomes in a deployed ICU setting.9,10 In this study, we found that HCCs were more likely to have closed vs. open ICU models, but that all were managed by a medical director and head nurse. HCCs were more likely to have a critical care committee than were LCCs. The majority of all MHS respondents were unable to provide APACHE II scores and all reported low-mortality rates except for two LCCs. While lack of illness severity reporting, or availability for MHS critical care services, makes it difficult to compare, overall, these data suggest that patients in MHS ICUs are less ill than patients admitted to civilian ICUs where the average admission APACHE II score is 19, and mortality rates average 9%.7 There were two LCCs that reported mortality rates higher than other respondents. Both reported their primary ICU as “cardiac” and did not provide APACHE II scores. The service that reported 11–15% mortality also reported the highest number of patient transfers to a higher level of care (50 patients) during the 6 mo prior to survey. The respondent with the highest mortality rate (16–20%) reported no unique characteristics. There seems to be notable differences in resource availability between HCCs and LCCs. Bedside nurses appear to be more available in the LCCs during MDR than they are at HCCs. Also, there appears to be more availability of ancillary services to support MDR in the LCCs. Increased availability of multidisciplinary care teams has been associated with decreased mortality rates and better outcomes, potentially mitigating the reduced availability of critical care trained physicians at LCCs in the MHS.5 The number of critical care protocols available can be a measure of how services have institutionalized common processes and established clinical practice guidelines. HCCs reported more protocols than LCCs and subjectively found them to be more effective than LCCs. However, human resource availabilities at LCCs may also balance the increased protocol availability and their perceived effectiveness at HCCs in terms of reducing mortality.7 HCCs were more likely to use checklists during MDR than LCCs, but it was highly physician dependent. This highlights that some important critical care processes in MHS ICUs may be personality dependent vice standardized. A recent study has shown that when a checklist was encouraged but not mandatory, the process had no impact on outcomes.12 We sought to better understand service capabilities by asking services for data about the number of patients transferred to other facilities. Although we could not calculate transfer rates as part of this study, the apparent difference in the volume of patient transfers between HCCs and LCCs is expected and is likely related to less availability of sub-specialty expertise at LCCs. This study is the first to successfully describe MHS critical care services and it highlights significant differences between organizational structure and resourcing between high and low capacity MTFs. Previous studies of MHS critical care services, such as the Structural Characteristics of DOD Intensive Care Units study conducted by Lockheed Martin in the fall of 2010, were unable to draw accurate or meaningful conclusions about MHS critical care services due to inaccuracies of administrative data (personal correspondence of JCP with MHS Clinical Quality Management Report on Intensive Care Units: Influence of Organizational Structure on Patient Outcomes, 2010). The most significant limitation of this study was the nature of its data reporting. Because there is no centralized repository for critical care related data, the study relied heavily on respondents identifying necessary information from local data sources and personal experience. In many cases, data sources included paper logbooks (personal communication of the PI, JCP, with POCs). Many respondents were unable to provide all of the information requested. Consequently, there was significant variability in the reporting of data to this survey making our findings only descriptive: definitive conclusions about critical care and its delivery in the MHS cannot be made based on this study. This limitation is, perhaps, the most important finding of this study. Across the MHS, information necessary to make important resourcing and process improvement decisions is not collected or is not easily accessible to clinical leadership. Inconsistent situational awareness of demographic and outcome data, as well the apparently high variability of critical care processes, personnel, and leadership makes it difficult to improve the quality and safety of patient care in ICUs across the MHS. This type of knowledge and understanding about structure, process, and outcomes has been demonstrated to impact patient outcome in critical and trauma care.7,11 Information about these additional aspects of MHS critical care (i.e., local data collection and review processes, personnel and leadership continuity) should be included in future studies. The Joint Trauma System has demonstrated through intentional monitoring of care processes and patient outcomes in combination with regular review of these data and discussion of opportunities to improve the ability to modify old processes or introduce new, more effective ones. Consequently, it facilitates system wide changes that result in improved patient outcomes over relatively short periods of time.11 This is the first study to describe the care models, patient populations, and clinical practices of MHS critical care services. Despite the study’s limitations, important information can be gleaned about how the MHS delivers critical care. First, the MHS does not have a centralized or consistent approach to critical care services across the enterprise. Second, demographic and outcomes data for critical care patients are not consistently available or easily accessible to clinical leadership in critical care. Third, many critical care processes or resources supported by evidenced-based literature are not consistently implemented or available across the enterprise. Given the significance of critical care to hospital costs, its impact on patient outcomes, and the likely association of critical care exposure to wartime skills retention, it is imperative that the MHS work to better understand these services across the enterprise in order to improve process and outcomes for its beneficiaries. This need is particularly important to combat casualty care during the interim between wars.13 Table V. Reported ICU Mortality Rates for HCCs and LCCs Mortality Rate Mortality Rate (%) HCC Services (n = 13) LCC Services (n = 14) Total <5 9 10 19 5–10 3 0 3 11–15 0 1 1 16–20 0 1 1 Unknown 1 2 3 Mortality Rate Mortality Rate (%) HCC Services (n = 13) LCC Services (n = 14) Total <5 9 10 19 5–10 3 0 3 11–15 0 1 1 16–20 0 1 1 Unknown 1 2 3 HCCs, high capacity center; LCCs, low capacity center. Table V. Reported ICU Mortality Rates for HCCs and LCCs Mortality Rate Mortality Rate (%) HCC Services (n = 13) LCC Services (n = 14) Total <5 9 10 19 5–10 3 0 3 11–15 0 1 1 16–20 0 1 1 Unknown 1 2 3 Mortality Rate Mortality Rate (%) HCC Services (n = 13) LCC Services (n = 14) Total <5 9 10 19 5–10 3 0 3 11–15 0 1 1 16–20 0 1 1 Unknown 1 2 3 HCCs, high capacity center; LCCs, low capacity center. Authors’ Contributions JJN participated in data analysis, wrote the draft manuscript, and participated in multiple pre-submission revisions of the manuscript. CJC helped conceive of the study idea, study protocol, and survey items in addition to analyzing data and being a primary writer and editor of the manuscript. CAM helped conceive of the study idea and study protocol, in addition to analyzing data and editing the manuscript. EAM-S helped conceive of the study idea and study protocol, in addition to analyzing data and editing the manuscript. FB completed the first analysis of the data, produced the initial data tables and abstract for presentation and this manuscript and assisted with editing the manuscript. AWB helped develop the study protocol, analyze data and edit the manuscript. KD helped conceive of the study idea and study protocol, in addition to analyzing data and editing the manuscript. JKA performed statistical support for the study protocol, statistical analysis of the data, and helped edit the manuscript. KKC helped conceive of the study idea and study protocol, in addition to analyzing data and editing the manuscript. MSM helped conceive of the study idea and study protocol, in addition to analyzing data and editing the manuscript. JCP was the principal investigator and originator for the study concept and protocol. He helped analyze the data, write and edit the manuscript, and gave final approval for its submission. Acknowledgements Mrs. Nicole Caldwell, U.S. Army Institute of Surgical Research, Fort Sam Houston, TX, USA, for her support with maintaining research and regulatory files. All of the following for contributing to the coordination and survey responses for this project: MAJ Craig Ainsworth MD, William Beaumont Army Medical Center, El Paso, TX, USA. MAJ Alain Abellada MD, Blanchfield Army Community Hospital, Fort Campbell, KY, USA. COL Stephen Silvey MD, MAJ Scott Trexler MD, LTC(P) David Bell MD, Houmayoun Ahmadian DO, and Col(ret) Jeffrey McNeil MD, Brooke Army Medical Center, JBSA Fort Sam Houston, TX, USA. COL Harold Thomas MD, Darnell Army Medical Center, Fort Hood, TX, USA. Dr. Michael Cole MD, Fort Belvoir Community Hospital, Fort Belvoir, VA, USA. LTC Sean Reilly MD, Landstuhl Regional Medical Center, Landstuhl, Germany. LTC Larry Linville MSN, General Leonard Wood Army Community Hospital, Fort Leonard Wood, MO, USA. Dr. Bruce Lovins MD, Martin Army Community Hospital, Fort Benning, GA, USA. LTC Jessica Bunin MD, MAJ Matthew Aboudara MD, Tripler Army Medical Center, Honolulu, HI, USA. COL Stewart McCarver MD, Walter Reed National Military Medical Center, Bethesda, MD, USA. MAJ Douglas Powell MD, Womack Army Medical Center, Fort Bragg, NC, USA. Col Brian Delmonaco MD, Mike O’Callaghan Federal Medical Center, Nellis AFB, NV, USA. Maj John Untisz DO, Eglin Air Force Base Hospital, Eglin AFB, FL, USA. Maj Tokunbo Matthews MD, Joint Base Elmendorf-Richardson Hospital, Anchorage, AK, USA. Ltc Christopher Dennis DO, David Grant US Air Force Medical Center, Travis AFB, CA, USA. MAJ Dara Regn MD, Wright-Patterson Medical Center, Dayton, OH, USA. Dr. Shawn French MD, Keesler Medical Center, Keesler AFB, MS. LCDR Thuy Lin MD, Naval Medical Center Portsmouth, Portsmouth, VA, USA. LCDR Ryan Maves MD, Naval Medical Center San Diego, San Diego, CA, USA. LCDR Russell Miller MD, Naval Hospital Camp Pendleton, Camp Pendleton, CA, USA. Dr. James Prahl MD, Naval Hospital Guam, Tutuhan, Guam. Supplementary data Supplementary data are available at Military Medicine online. References 1 Halpern NA , Pastores SM : Critical care medicine in the United States 2000–2005: an analysis of bed numbers, occupancy rates, payer mix, and costs . Crit Care Med 2010 ; 38 : 65 – 71 . Google Scholar CrossRef Search ADS PubMed 2 Barnato AE , Kahn JM , Rubenfeld GD , McCauley K , Fontaine D , Frassica JJ , et al. : Prioritizing the organization and management of intensive care services in the United States: the PrOMIS Conference. 2007 . pp. 1003–11. 3 Harvey MA , Penoyer DA , Jastremski C : “Building Teamwork to Improve Outcomes.”. In: Textbook of Critical Care: Edition , pp 1589 – 94 . Edited by Vincent JL , Abraham E , Kochanek P , Moore F , Fink M . Philadelphia , Saunders , 2011 . Print. Google Scholar CrossRef Search ADS 4 Chalfin DB , Cohen IL , Lambrinos J : The economics and cost-effectiveness of critical care medicine . Intensive Care Med 1995 ; 21 : 952 – 61 . Google Scholar CrossRef Search ADS PubMed 5 Levy MM , Dellinger RP , Townsend SR , Linde-Zwirble WT , Marshall JC , Bion J , et al. : The Surviving Sepsis Campaign: results of an international guideline-based performance improvement program targeting severe sepsis . Crit Care Med 2010 ; 38 : 367 – 74 . Google Scholar CrossRef Search ADS PubMed 6 The United Hospital Fund , “Critical Care Leadership Network”. Available at https://uhfnyc.org/initiatives/quality_improvement/critical-care-leadership-network, accessed 26 October 2017 . 7 Checkley W , Martin GS , Brown SM , Chang SY , Dabbagh O , Fremont RD , et al. : Structure, process, and annual ICU mortality across 69 centers: United States Critical Illness and Injury Trials Group Critical Illness Outcomes Study . Crit Care Med 2014 ; 42 : 344 – 56 . Google Scholar CrossRef Search ADS PubMed 8 Weled BJ , Adzhigirey LA , Hodgman TM , Brilli RJ , Spevetz A , Kline AM , et al. : Critical care delivery: the importance of process of care and ICU structure to improved outcomes: an update from the American College of critical care medicine task force on models of critical care . Crit Care Med 2015 ; 43 : 1520 – 5 . Google Scholar CrossRef Search ADS PubMed 9 Lettieri CJ , Shah AA , Greenburg DL : An intensivist-directed intensive care unit improves clinical outcomes in a combat zone . Crit Care Med 2009 ; 37 : 1256 – 60 . Google Scholar CrossRef Search ADS PubMed 10 Grathwohl KW , Venticinque SG : Organizational characteristics of the austere intensive care unit: the evolution of military trauma and critical care medicine; applications for civilian medical care systems . Crit Care Med 2008 ; 36 : S275 – 83 . Google Scholar CrossRef Search ADS PubMed 11 Palm K , Apodaca A , Spencer D , Costanzo G , Bailey J , Fortuna G , et al. : Evaluation of military trauma system practices related to complications after injury . J Trauma Acute Care Surg 2012 ; 73 : S465 – 71 . Google Scholar CrossRef Search ADS PubMed 12 Urbach DR , Govindarajan A , Saskin R , Wilton AS , Baxter NN : Introduction of surgical safety checklists in Ontario, Canada . N Engl J Med 2014 ; 370 : 1029 – 38 . Google Scholar CrossRef Search ADS PubMed 13 Rasmussen TE , Gross KR , Baer DG : Where do we go from here? J Trauma Acute Care Surg 2013 ; 75 : S105 – 6 . Google Scholar CrossRef Search ADS PubMed Author notes The views expressed are those of the author(s) and do not reflect the official policy or position of the U.S. Army Medical Department, Department of the Army, Department of the Air Force, Department of the Navy, Department of Defense, or the U.S. Government. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Military Medicine Oxford University Press

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Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018.
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Abstract

Abstract Introduction Critical care is an important component of in-patient and combat casualty care, and it is a major contributor to U.S. healthcare costs. Regular exposure to critically ill and injured patients may directly contribute to wartime skills retention for military caregivers. Data describing critical care services in the Military Health System (MHS), however, is lacking. This study was undertaken to describe MHS critical care services, their resource utilization, and differences in care practices amongst military treatment facilities (MTFs). Materials and Methods Twenty-six MTFs representing 38 adult critical care services or intensive care units (ICUs) were surveyed. The survey collected information about organizational structure, resourcing, and unit characteristics at the time of a concurrent 24-h point-prevalence survey designed to describe patient characteristics and staffing in these facilities. The survey was anonymous and protected health information was not collected. We analyzed the data according to high capacity centers (HCCs) (≥200 beds) and low capacity centers (LCCs) (<200 beds). Differences between HCCs and LCCs were compared using Fisher’s exact test. Results Seventeen MTFs (7 HCCs and 10 LCCs), representing 27 ICUs, responded to the survey. This was a 65% response rate for MTFs and a 71% response rate for services/ICUs. HCCs reported more closed vs. open ICUs; more dedicated critical care services (i.e., medical and surgical ICUs vs. mixed ICUs); fewer respiratory therapists available, but more with certification; more total nursing staff and more critical care certified nurses; the use of subjectively more effective protocols (10.5 vs. 6.7 protocols/unit or service); higher utilization of an ICU daily rounds checklist (65% vs. 0%); and less consistency of clinician type participation during multidisciplinary rounds. ICU leadership structure was similar among the institutions. The majority of respondents were unable to provide summary APACHE II scores, but HCCs were more likely to submit this information than LCCs. Most centers perform multidisciplinary rounds daily, but they are more likely to be run by a physician credentialed in critical care at HCCs (85% vs. 59%, p < 0.05). 67% of respondents reported mortality rates <5%. The two services that reported mortality rates greater than 10% were both LCCs. Conclusion This is the first comprehensive report about MHS critical care services. Despite notable variability in data reporting, an important finding itself, this study highlights notable differences in organizational structure and resourcing between HCCs and LCCs within the MHS. The clinical implication of these differences (i.e., impact on patient outcomes) of these differences require further study. Better understanding of MHS critical care services may improve enterprise decision-making about these services which could ultimately improve care of combat casualties. INTRODUCTION In the USA, over 5 million patients are admitted annually to the intensive care unit (ICU), which constitutes one of the largest portions of national healthcare expenditures. These costs increased from $56.6 to $81.7 billion from 2000 to 2005 and are predicted to continue to rise as the number of patients over age 65 will double between 2007 and 2030.1,2 ICU admission and subsequent critical care represent approximately 13% of all hospital costs, 4% of national health expenditures, and 1% of the U.S. gross domestic product.3,4 According to Halpern, each day of ICU care costs on average $3500 per patient.1 Considering the high volume of critically ill patients with increased complexity, and the significant cost in providing this care, it is imperative that organizations understand critical care services in order to streamline care processes, minimize costs, and ultimately optimize patient safety and outcome. There are 26 military treatment facilities (MTFs) with approximately 300–400 total ICU beds that provide critical care services in the Military Health System (MHS). Unfortunately, little is known about these services as past efforts to accurately describe them have failed.6 There are no dedicated databases that describe critical care services for the enterprise and local MTF data are difficult to identify. There is also no centralized or enterprise level leadership or organizational structure specific to critical care: instead, critical care leadership and services are fractured among more traditional organizational lines of accountability: surgery, medicine, anesthesia, and nursing. This fractured approach to critical care for the MHS makes it difficult to identify and collect important data that might influence how the military delivers this important service to its beneficiaries and warfighter. The primary objective of this observational study was to produce an accurate snapshot of current critical care services, capacity, resources, quality indicators, and critical care outcomes for critical care leaders across the MHS. METHODS We performed an anonymous study of 26 MTFs (15 Army, 7 Air Force, and 4 Navy) representing a total of 38 adult critical care services or physical ICUs. The study consisted of two parts: an administrative survey and a point-prevalence study of all patients admitted to the unit/service during a 24-h period. The study was submitted to the U.S. Army Institute of Surgical Research’s Regulatory Office and determined to be research not involving human subjects. This manuscript reports the results of the administrative survey portion of the study only. The results of the point-prevalence study are reported separately in this journal. The administrative survey was designed to help identify the major differences between institutions with respect to care models, patient populations, and management practices and was modeled after similar surveys created by the United States Critical Illness and Injuries Trials Group Critical Illness Outcomes Study (USCIITG-CIOS) and the New York Hospital Association Critical Care Leaders Network (NYHA CCLN).6,7 The survey collected data about organizational structure/resourcing, unit characteristics, patient population, and the availability and perceived effectiveness of critical care protocols, (see Supplementary Material for the survey included with the online content for this manuscript). Prior to administration, the survey underwent a process for face validation and questions that were not valid were revised. Points-of-contact involved in critical care at each facility completed the survey at any time during the month prior to or the month following the 24-h prevalence study. We asked all MHS MTFs that have designated critical care beds to participate in this survey. At the beginning of the study, the principal investigator (PI, author JCP) contacted each MTF to identify ICU leadership and their contact information. An e-mail was sent to these points-of-contact (POCs) with a request to participate. Instructions for completing the survey were also included. In order to determine how many centers participated, we asked identified POCs at each center to declare by e-mail if their center participated, but individual center survey data were kept anonymous. To maintain anonymity of data, POCs created a self-identified code name, known only by the center POCs, to be used in the survey in order to aggregate data for a single facility if that facility reported from multiple critical care services or separate ICUs. In other words, large centers could have each ICU service (i.e., medical, surgical, trauma critical care service) or unit (i.e., medical ICU, surgical ICU, etc.) respond to the survey, but each service/unit identified itself with the site-specific code name. For the purposes of the survey, a service was defined as a dedicated physician team whereas a unit was a physical location that may or may not be dedicated to a specific patient population. A master list of physical sites and their associated code names was not created and POC information was not collected in the survey. Thus, only POCs from a facility could identify which data belonged to that facility. The surveys were completed by POCs or POC designee(s) at each MTF. This POC was usually a medical director or a physician/nurse leader identified by the PI through networking with key leaders in critical care throughout the MHS. POCs worked with leaders of other multidisciplinary groups to identify the most reliable source of information for the survey. POCs personally vouched for the accuracy of the data. A list of POCs and his/her facility is in the Acknowledgements section. In situations where the requested information was not available, POCs could answer as such. We analyzed the data according to high capacity centers (HCCs) and low capacity centers (LCCs). HCCs were defined as having greater than 200 beds, whereas LCCs had less than or equal to 200 beds. This cutoff was chosen to separate the larger, academic referral medical centers from the smaller MTFs that more closely represent community hospitals. We further described centers in terms of service/unit types, care models, resources, protocol availability, and population data/unit demographics. Differences between HCCs and LCCs were compared using Fisher’s exact test with significance set at p < 0.05. RESULTS Seventeen (65% response rate) of MTFs responded to the survey representing 27 ICUs (71% response rate) across the MHS. Respondents from HCCs more often reported on behalf of a service, whereas respondents from LCCs more often reported as a physical unit. HCCs more often reported having multiple “owners,” or stakeholders (median of 2; examples include the Department of Medicine, the Department of Surgery, Pulmonary Critical Care Service, Anesthesia and Operative Services, etc.), of their critical care services than LCCs (median of 1). The median annual admissions for a HCC were 500–1000 patients; whereas, the median annual admissions for LCCs were 400–500 with three locations reporting no data (Table I). Table I. Reporting Characteristics of MHS Critical Care Services Unit/Service Reporting Characteristics Label Bedsa Physical ICUs # Services “Owners” of CCa Annual Admissionsa Average Daily Censusa Open vs. Closed Admitting Crit Care Consultation Average APACHE Score (Admission)a HC-1 201–300 3 2 2 1000–2000 6–10 Open By Criteria 11–15 HC-2 201–300 3 4 2 100–1000 6–10 Mixed By Criteria 11–15 HC-3 201–300 2 3 2 500–1000 3–5 Closed Closed UNK HC-4 401–500 8 6 3–7 100–2000 6–15 4C, 2O Closed or All 21–25; 5 x UNK Medians 200–300 3 4 2 500–1000 6–10 46% C 42% Closed 54% UNK LC-1 <50 1 2 1 500–1000 1–2 Open By Criteria 36–40 LC-2 <50 1 3 1 300–400 1–2 Mixed All 6–10 LC-3 50–100 1 0 1 500–1000 3–5 Open None UNK LC-4 50–100 1 0 3 300–400 1–2 Open None 6–10 LC-5 50–100 1 1 1 500–1000 3–5 Open All UNK LC-6 50–100 1 3 1 500–1000 6–10 Open By Criteria UNK LC-7 50–100 1 1 2 200–300 1–2 Open By Criteria UNK LC-8 50–100 1 2 1 50–100 1–2 Open By Criteria 6–10 LC-9 50–100 1 3 1 UNK 6–10 Mixed None UNK LC-10 50–100 1 0 3 400–500 3–5 Open None UNK LC-11 50–100 0 0 1 UNK 3–5 Open None UNK LC-12 101–200 1 1 1 300–400 3–5 Open Svc Dependent UNK LC-13 101–200 1 1 1 UNK 3–5 Closed All UNK LC-14 101–200 1 1 1 400–500 3–5 Closed Closed UNK Medians 50–100 1 1 1 400–500(3xUNK) 3–5 14% C 36% None 71% UNK Unit/Service Reporting Characteristics Label Bedsa Physical ICUs # Services “Owners” of CCa Annual Admissionsa Average Daily Censusa Open vs. Closed Admitting Crit Care Consultation Average APACHE Score (Admission)a HC-1 201–300 3 2 2 1000–2000 6–10 Open By Criteria 11–15 HC-2 201–300 3 4 2 100–1000 6–10 Mixed By Criteria 11–15 HC-3 201–300 2 3 2 500–1000 3–5 Closed Closed UNK HC-4 401–500 8 6 3–7 100–2000 6–15 4C, 2O Closed or All 21–25; 5 x UNK Medians 200–300 3 4 2 500–1000 6–10 46% C 42% Closed 54% UNK LC-1 <50 1 2 1 500–1000 1–2 Open By Criteria 36–40 LC-2 <50 1 3 1 300–400 1–2 Mixed All 6–10 LC-3 50–100 1 0 1 500–1000 3–5 Open None UNK LC-4 50–100 1 0 3 300–400 1–2 Open None 6–10 LC-5 50–100 1 1 1 500–1000 3–5 Open All UNK LC-6 50–100 1 3 1 500–1000 6–10 Open By Criteria UNK LC-7 50–100 1 1 2 200–300 1–2 Open By Criteria UNK LC-8 50–100 1 2 1 50–100 1–2 Open By Criteria 6–10 LC-9 50–100 1 3 1 UNK 6–10 Mixed None UNK LC-10 50–100 1 0 3 400–500 3–5 Open None UNK LC-11 50–100 0 0 1 UNK 3–5 Open None UNK LC-12 101–200 1 1 1 300–400 3–5 Open Svc Dependent UNK LC-13 101–200 1 1 1 UNK 3–5 Closed All UNK LC-14 101–200 1 1 1 400–500 3–5 Closed Closed UNK Medians 50–100 1 1 1 400–500(3xUNK) 3–5 14% C 36% None 71% UNK Examples of “owners” include the Department of Medicine, the Department of Surgery, Pulmonary Critical Care Service, Anesthesia and Operative Services, etc. Average daily census is for each service or physical unit in HCCs and for the physical ICU in the LCCs. Thus, total census for HC-3 with two physical ICUs and three services would be 6–15. C, Closed; CC, critical care; O, open; UNK, unknown. aRanges in this table indicate all responses provided to the survey. In some cases, multiple services within one organization responded yielding wide variation in response. For example, HC-4 indicated that one service only had 100 admissions per year whereas another had up to 2000. Response options can be reviewed in the supplementary material. Table I. Reporting Characteristics of MHS Critical Care Services Unit/Service Reporting Characteristics Label Bedsa Physical ICUs # Services “Owners” of CCa Annual Admissionsa Average Daily Censusa Open vs. Closed Admitting Crit Care Consultation Average APACHE Score (Admission)a HC-1 201–300 3 2 2 1000–2000 6–10 Open By Criteria 11–15 HC-2 201–300 3 4 2 100–1000 6–10 Mixed By Criteria 11–15 HC-3 201–300 2 3 2 500–1000 3–5 Closed Closed UNK HC-4 401–500 8 6 3–7 100–2000 6–15 4C, 2O Closed or All 21–25; 5 x UNK Medians 200–300 3 4 2 500–1000 6–10 46% C 42% Closed 54% UNK LC-1 <50 1 2 1 500–1000 1–2 Open By Criteria 36–40 LC-2 <50 1 3 1 300–400 1–2 Mixed All 6–10 LC-3 50–100 1 0 1 500–1000 3–5 Open None UNK LC-4 50–100 1 0 3 300–400 1–2 Open None 6–10 LC-5 50–100 1 1 1 500–1000 3–5 Open All UNK LC-6 50–100 1 3 1 500–1000 6–10 Open By Criteria UNK LC-7 50–100 1 1 2 200–300 1–2 Open By Criteria UNK LC-8 50–100 1 2 1 50–100 1–2 Open By Criteria 6–10 LC-9 50–100 1 3 1 UNK 6–10 Mixed None UNK LC-10 50–100 1 0 3 400–500 3–5 Open None UNK LC-11 50–100 0 0 1 UNK 3–5 Open None UNK LC-12 101–200 1 1 1 300–400 3–5 Open Svc Dependent UNK LC-13 101–200 1 1 1 UNK 3–5 Closed All UNK LC-14 101–200 1 1 1 400–500 3–5 Closed Closed UNK Medians 50–100 1 1 1 400–500(3xUNK) 3–5 14% C 36% None 71% UNK Unit/Service Reporting Characteristics Label Bedsa Physical ICUs # Services “Owners” of CCa Annual Admissionsa Average Daily Censusa Open vs. Closed Admitting Crit Care Consultation Average APACHE Score (Admission)a HC-1 201–300 3 2 2 1000–2000 6–10 Open By Criteria 11–15 HC-2 201–300 3 4 2 100–1000 6–10 Mixed By Criteria 11–15 HC-3 201–300 2 3 2 500–1000 3–5 Closed Closed UNK HC-4 401–500 8 6 3–7 100–2000 6–15 4C, 2O Closed or All 21–25; 5 x UNK Medians 200–300 3 4 2 500–1000 6–10 46% C 42% Closed 54% UNK LC-1 <50 1 2 1 500–1000 1–2 Open By Criteria 36–40 LC-2 <50 1 3 1 300–400 1–2 Mixed All 6–10 LC-3 50–100 1 0 1 500–1000 3–5 Open None UNK LC-4 50–100 1 0 3 300–400 1–2 Open None 6–10 LC-5 50–100 1 1 1 500–1000 3–5 Open All UNK LC-6 50–100 1 3 1 500–1000 6–10 Open By Criteria UNK LC-7 50–100 1 1 2 200–300 1–2 Open By Criteria UNK LC-8 50–100 1 2 1 50–100 1–2 Open By Criteria 6–10 LC-9 50–100 1 3 1 UNK 6–10 Mixed None UNK LC-10 50–100 1 0 3 400–500 3–5 Open None UNK LC-11 50–100 0 0 1 UNK 3–5 Open None UNK LC-12 101–200 1 1 1 300–400 3–5 Open Svc Dependent UNK LC-13 101–200 1 1 1 UNK 3–5 Closed All UNK LC-14 101–200 1 1 1 400–500 3–5 Closed Closed UNK Medians 50–100 1 1 1 400–500(3xUNK) 3–5 14% C 36% None 71% UNK Examples of “owners” include the Department of Medicine, the Department of Surgery, Pulmonary Critical Care Service, Anesthesia and Operative Services, etc. Average daily census is for each service or physical unit in HCCs and for the physical ICU in the LCCs. Thus, total census for HC-3 with two physical ICUs and three services would be 6–15. C, Closed; CC, critical care; O, open; UNK, unknown. aRanges in this table indicate all responses provided to the survey. In some cases, multiple services within one organization responded yielding wide variation in response. For example, HC-4 indicated that one service only had 100 admissions per year whereas another had up to 2000. Response options can be reviewed in the supplementary material. The results show a wide variation in practice and patient census among critical care services in the MHS. HCCs reported more ICUs dedicated to a specific patient population (e.g., medical or surgical) whereas LCCs reported more mixed population ICUs. HCCs reported more closed vs. open ICUs as a care model, more access to critical care trained physicians (100% vs. 64%), and more dedicated critical care services (i.e., Medical and Surgical ICUs vs. Mixed ICUs) (Table I). The majority of respondents were unable to provide APACHE II scores making it difficult to describe the complexity of critical illness cared for by MHS critical care services over the last year. Only 30% of HCCs and 28% of LCC were able to report average APACHE II scores for the past year. Of the LCCs that reported APACHE II scores, there were three centers that reported median ranges of 6–10 (approximate 8% mortality); one LCC reported a median APACHE II range of 36–40 (approximate 75% mortality). Of the HCCs services that reported, two services reported an APACHE II range of 11–15, and one service reported a range of 21–25 (Table I). HCCs reported fewer respiratory therapists per service but more with registered respiratory therapist (RRT) certification. HCCs also reported more nursing staff and slightly more nurses with the critical care registered nursing (CCRN) certification. LCCs reported more nursing experience by median range for years of experience (Table II). The presence of the multidisciplinary care team during multidisciplinary rounds (MDR) differed between HCCs and LCCs. Nurses are more frequently present during MDR at LCCs (86% vs. 54%), as are ancillary or specialty support staff. There are significant differences in the availability of dietitians (64% vs. 0%, p < 0.05), pharmacists (71% vs. 0%, p < 0.05), and case manager/social workers (36% vs. 0%, p < 0.05) in LCCs vs. HCCs. However, there is a wider range of ancillary or specialty support type in the HCCs (Table III). For example, HCCs reported more presence of medical students, resident physicians, and mid-level providers than did LCCs. Table II. Staffing Resources Across the MHS Critical Care Services Staffing Location Nurses on Staff % Nurses CCRN Certified Average Nursing Experience RTs Available to the ICU % RTs RRT Certified HCC-1 41–50 51–60 6–10 1–2 91–99 HCC-2 41–50 31–40 1–5 6–10 71–80 HCC-3 41–60 41–50 1–5 1–2 100 HCC-4 31 to >80 21–70 6–10 3–20 <5 to 80 Median 41–50 41–60 1–10 Variable Variable LCC-1 12 <5 6–10 10–15 51–60 LCC-2 6–10 91–99 1–5 6–10 51–60 LCC-3 31–40 UNK 11–20 6–10 100 LCC-4 16–20 21–30 6–10 3–5 71–80 LCC-5 31–40 41–50 11–20 16–20 41–50 LCC-6 16–20 81–90 6–10 6–10 81–90 LCC-7 11–15 41–50 11–20 10–15 11–20 LCC-8 11–15 41–50 6–10 16–20 5–10 LCC-9 16–20 41–50 1–5 1–2 41–50 LCC-10 16–20 41–50 6–10 10–15 <5 LCC-11 11–15 61–70 6–10 6–10 41–50 LCC-12 21–30 81–90 UNK 6–10 UNK LCC-13 3–5 81–90 1–5 1–2 100 LCC-14 21–30 61–70 6–10 16–20 61–70 Median 16–20 41–50 (4 > 80%) 6–10 6–10 51–60 Staffing Location Nurses on Staff % Nurses CCRN Certified Average Nursing Experience RTs Available to the ICU % RTs RRT Certified HCC-1 41–50 51–60 6–10 1–2 91–99 HCC-2 41–50 31–40 1–5 6–10 71–80 HCC-3 41–60 41–50 1–5 1–2 100 HCC-4 31 to >80 21–70 6–10 3–20 <5 to 80 Median 41–50 41–60 1–10 Variable Variable LCC-1 12 <5 6–10 10–15 51–60 LCC-2 6–10 91–99 1–5 6–10 51–60 LCC-3 31–40 UNK 11–20 6–10 100 LCC-4 16–20 21–30 6–10 3–5 71–80 LCC-5 31–40 41–50 11–20 16–20 41–50 LCC-6 16–20 81–90 6–10 6–10 81–90 LCC-7 11–15 41–50 11–20 10–15 11–20 LCC-8 11–15 41–50 6–10 16–20 5–10 LCC-9 16–20 41–50 1–5 1–2 41–50 LCC-10 16–20 41–50 6–10 10–15 <5 LCC-11 11–15 61–70 6–10 6–10 41–50 LCC-12 21–30 81–90 UNK 6–10 UNK LCC-13 3–5 81–90 1–5 1–2 100 LCC-14 21–30 61–70 6–10 16–20 61–70 Median 16–20 41–50 (4 > 80%) 6–10 6–10 51–60 UNK, unknown. Table II. Staffing Resources Across the MHS Critical Care Services Staffing Location Nurses on Staff % Nurses CCRN Certified Average Nursing Experience RTs Available to the ICU % RTs RRT Certified HCC-1 41–50 51–60 6–10 1–2 91–99 HCC-2 41–50 31–40 1–5 6–10 71–80 HCC-3 41–60 41–50 1–5 1–2 100 HCC-4 31 to >80 21–70 6–10 3–20 <5 to 80 Median 41–50 41–60 1–10 Variable Variable LCC-1 12 <5 6–10 10–15 51–60 LCC-2 6–10 91–99 1–5 6–10 51–60 LCC-3 31–40 UNK 11–20 6–10 100 LCC-4 16–20 21–30 6–10 3–5 71–80 LCC-5 31–40 41–50 11–20 16–20 41–50 LCC-6 16–20 81–90 6–10 6–10 81–90 LCC-7 11–15 41–50 11–20 10–15 11–20 LCC-8 11–15 41–50 6–10 16–20 5–10 LCC-9 16–20 41–50 1–5 1–2 41–50 LCC-10 16–20 41–50 6–10 10–15 <5 LCC-11 11–15 61–70 6–10 6–10 41–50 LCC-12 21–30 81–90 UNK 6–10 UNK LCC-13 3–5 81–90 1–5 1–2 100 LCC-14 21–30 61–70 6–10 16–20 61–70 Median 16–20 41–50 (4 > 80%) 6–10 6–10 51–60 Staffing Location Nurses on Staff % Nurses CCRN Certified Average Nursing Experience RTs Available to the ICU % RTs RRT Certified HCC-1 41–50 51–60 6–10 1–2 91–99 HCC-2 41–50 31–40 1–5 6–10 71–80 HCC-3 41–60 41–50 1–5 1–2 100 HCC-4 31 to >80 21–70 6–10 3–20 <5 to 80 Median 41–50 41–60 1–10 Variable Variable LCC-1 12 <5 6–10 10–15 51–60 LCC-2 6–10 91–99 1–5 6–10 51–60 LCC-3 31–40 UNK 11–20 6–10 100 LCC-4 16–20 21–30 6–10 3–5 71–80 LCC-5 31–40 41–50 11–20 16–20 41–50 LCC-6 16–20 81–90 6–10 6–10 81–90 LCC-7 11–15 41–50 11–20 10–15 11–20 LCC-8 11–15 41–50 6–10 16–20 5–10 LCC-9 16–20 41–50 1–5 1–2 41–50 LCC-10 16–20 41–50 6–10 10–15 <5 LCC-11 11–15 61–70 6–10 6–10 41–50 LCC-12 21–30 81–90 UNK 6–10 UNK LCC-13 3–5 81–90 1–5 1–2 100 LCC-14 21–30 61–70 6–10 16–20 61–70 Median 16–20 41–50 (4 > 80%) 6–10 6–10 51–60 UNK, unknown. Table III. The Frequency with Which Specified Clinician Types Are Present During Multidisciplinary Rounds in the ICU Clinicians Present on MDR Team Member Low Capacity High Capacity (Always %/Total %) (Always %/Total %) Attending/staff physician 100/100 100/100 Bedside Nurse 86/100 54/100 Dietician* 64/100 0/54 Pharmacist* 71/100 0/62 Charge nurse 57/86 15/54 Case manager or social worker* 36/93 0/38 Student 0/86 8/62 Resident physician (physician in training) 50/79 77/92 Respiratory therapist 29/86 15/46 Clinical nurse specialist 0/57 0/69 Head nurse 7/57 0/54 Physician extender (mid-level provider) 7/29 15/46 Physical therapist 7/29 8/8 Psychologist 7/14 0/0 Clinicians Present on MDR Team Member Low Capacity High Capacity (Always %/Total %) (Always %/Total %) Attending/staff physician 100/100 100/100 Bedside Nurse 86/100 54/100 Dietician* 64/100 0/54 Pharmacist* 71/100 0/62 Charge nurse 57/86 15/54 Case manager or social worker* 36/93 0/38 Student 0/86 8/62 Resident physician (physician in training) 50/79 77/92 Respiratory therapist 29/86 15/46 Clinical nurse specialist 0/57 0/69 Head nurse 7/57 0/54 Physician extender (mid-level provider) 7/29 15/46 Physical therapist 7/29 8/8 Psychologist 7/14 0/0 *p < 0.05 for comparison of protocol presence at HCCs vs. LCCs. Table III. The Frequency with Which Specified Clinician Types Are Present During Multidisciplinary Rounds in the ICU Clinicians Present on MDR Team Member Low Capacity High Capacity (Always %/Total %) (Always %/Total %) Attending/staff physician 100/100 100/100 Bedside Nurse 86/100 54/100 Dietician* 64/100 0/54 Pharmacist* 71/100 0/62 Charge nurse 57/86 15/54 Case manager or social worker* 36/93 0/38 Student 0/86 8/62 Resident physician (physician in training) 50/79 77/92 Respiratory therapist 29/86 15/46 Clinical nurse specialist 0/57 0/69 Head nurse 7/57 0/54 Physician extender (mid-level provider) 7/29 15/46 Physical therapist 7/29 8/8 Psychologist 7/14 0/0 Clinicians Present on MDR Team Member Low Capacity High Capacity (Always %/Total %) (Always %/Total %) Attending/staff physician 100/100 100/100 Bedside Nurse 86/100 54/100 Dietician* 64/100 0/54 Pharmacist* 71/100 0/62 Charge nurse 57/86 15/54 Case manager or social worker* 36/93 0/38 Student 0/86 8/62 Resident physician (physician in training) 50/79 77/92 Respiratory therapist 29/86 15/46 Clinical nurse specialist 0/57 0/69 Head nurse 7/57 0/54 Physician extender (mid-level provider) 7/29 15/46 Physical therapist 7/29 8/8 Psychologist 7/14 0/0 *p < 0.05 for comparison of protocol presence at HCCs vs. LCCs. Within the MHS, most centers perform MDR daily (HCCs 92% vs. LCCs 79%). However, these are more likely to be run by a physician certified in critical care at HCCs (85% vs. 59%, p < 0.05). MDR are less likely to occur on the weekends than during weekdays (HCCs 69% vs. LCCs 64%). HCCs are more likely to use checklists during MDR than LCCs (85% vs. 29%, p < 0.05), although in both it is attending physician dependent (HCCs 62% vs. LCCs 36%). HCCs had twice as many protocols as LCCs. When asked if protocols were present and effective/successful, HCCs reported more protocols than LCCs (10.5 vs. 6.7 protocols/unit or service). HCCs seemed to subjectively find more of their protocols to be effective as well; HCCs reported 62% of protocols to be effective vs. LCCs reporting 32% (p < 0.05) of protocols to be effective (Table IV). Table IV. Number of Respondents from HCCs and LCCs that Reported Present, Effective and Successful ICU Protocols vs. the Number Without the Protocol or with One Present that Does Not Effectively Achieve Its Goals Critical Care Protocols HCCs (n = 13) LCCs (n = 14) Present, Effective, Successful Absent or Ineffective Present, Effective, Successful Absent or Ineffective Unknown or Blank Response n (%) n (%) n (%) n (%) n (%) Standard ventilator management 3 (23) 10 (77) 4 (29) 8 (57) 2 Ventilator liberation 11 (85) 2 (15) 7 (50) 7 (50) 0 Respiratory adjunctive therapies 9 (69) 4 (31) 4 (29) 9 (64) 1 Sedation 10 (77) 3 (23) 8 (57) 6 (43) 0 Neuro-muscular paralysis 4 (31) 9 (69) 2 (14) 11 (79) 1 Sleep 5 (38) 8 (62) 3 (21) 11 (79) 0 Delirium management 5 (38) 8 (62) 6 (43) 8 (57) 0 Tight glycemic control* 13 (100) 0 (0) 4 (29) 8 (57) 2 Mobility* 11 (85) 2 (15) 1 (7) 13 (93) 0 Nutrition* 11 (85) 2 (15) 5 (36) 9 (64) 0 DVT prophylaxis 11 (85) 2 (15) 9 (64) 5 (36) 0 GI prophylaxis 9 (69) 4 (31) 8 (57) 6 (43) 0 Therapeutic hypothermia* 12 (92) 1 (8) 6 (43) 8 (57) 0 Euthermia* 7 (54) 6 (46) 1 (7) 12 (86) 1 TBI or brain injury management 6 (46) 7 (54) 0 (0) 14 (100) 0 Massive transfusion* 12 (92) 1 (8) 6 (43) 7 (50) 1 Sepsis 3 (23) 10 (77) 4 (29) 10 (71) 0 ECMO/ECLS* 4 (31) 9 (69) 0 (0) 14 (100) 0 TPE 3 (23) 10 (77) 0 (0) 14 (100) 0 Total 149 (60) 98 (40) 78 (29) 180 (68) 8 (3) Critical Care Protocols HCCs (n = 13) LCCs (n = 14) Present, Effective, Successful Absent or Ineffective Present, Effective, Successful Absent or Ineffective Unknown or Blank Response n (%) n (%) n (%) n (%) n (%) Standard ventilator management 3 (23) 10 (77) 4 (29) 8 (57) 2 Ventilator liberation 11 (85) 2 (15) 7 (50) 7 (50) 0 Respiratory adjunctive therapies 9 (69) 4 (31) 4 (29) 9 (64) 1 Sedation 10 (77) 3 (23) 8 (57) 6 (43) 0 Neuro-muscular paralysis 4 (31) 9 (69) 2 (14) 11 (79) 1 Sleep 5 (38) 8 (62) 3 (21) 11 (79) 0 Delirium management 5 (38) 8 (62) 6 (43) 8 (57) 0 Tight glycemic control* 13 (100) 0 (0) 4 (29) 8 (57) 2 Mobility* 11 (85) 2 (15) 1 (7) 13 (93) 0 Nutrition* 11 (85) 2 (15) 5 (36) 9 (64) 0 DVT prophylaxis 11 (85) 2 (15) 9 (64) 5 (36) 0 GI prophylaxis 9 (69) 4 (31) 8 (57) 6 (43) 0 Therapeutic hypothermia* 12 (92) 1 (8) 6 (43) 8 (57) 0 Euthermia* 7 (54) 6 (46) 1 (7) 12 (86) 1 TBI or brain injury management 6 (46) 7 (54) 0 (0) 14 (100) 0 Massive transfusion* 12 (92) 1 (8) 6 (43) 7 (50) 1 Sepsis 3 (23) 10 (77) 4 (29) 10 (71) 0 ECMO/ECLS* 4 (31) 9 (69) 0 (0) 14 (100) 0 TPE 3 (23) 10 (77) 0 (0) 14 (100) 0 Total 149 (60) 98 (40) 78 (29) 180 (68) 8 (3) Bold and * indicates p < 0.05 for comparison of protocol presence at HCCS vs. LCCs. DVT, deep vein thrombosis; ECLS, extracorporeal life support; ECMO, extracorporeal membrane oxygenation; TBI, traumatic brain injury; GI, gastrointestinal; TPE, therapeutic plasma exchange. Table IV. Number of Respondents from HCCs and LCCs that Reported Present, Effective and Successful ICU Protocols vs. the Number Without the Protocol or with One Present that Does Not Effectively Achieve Its Goals Critical Care Protocols HCCs (n = 13) LCCs (n = 14) Present, Effective, Successful Absent or Ineffective Present, Effective, Successful Absent or Ineffective Unknown or Blank Response n (%) n (%) n (%) n (%) n (%) Standard ventilator management 3 (23) 10 (77) 4 (29) 8 (57) 2 Ventilator liberation 11 (85) 2 (15) 7 (50) 7 (50) 0 Respiratory adjunctive therapies 9 (69) 4 (31) 4 (29) 9 (64) 1 Sedation 10 (77) 3 (23) 8 (57) 6 (43) 0 Neuro-muscular paralysis 4 (31) 9 (69) 2 (14) 11 (79) 1 Sleep 5 (38) 8 (62) 3 (21) 11 (79) 0 Delirium management 5 (38) 8 (62) 6 (43) 8 (57) 0 Tight glycemic control* 13 (100) 0 (0) 4 (29) 8 (57) 2 Mobility* 11 (85) 2 (15) 1 (7) 13 (93) 0 Nutrition* 11 (85) 2 (15) 5 (36) 9 (64) 0 DVT prophylaxis 11 (85) 2 (15) 9 (64) 5 (36) 0 GI prophylaxis 9 (69) 4 (31) 8 (57) 6 (43) 0 Therapeutic hypothermia* 12 (92) 1 (8) 6 (43) 8 (57) 0 Euthermia* 7 (54) 6 (46) 1 (7) 12 (86) 1 TBI or brain injury management 6 (46) 7 (54) 0 (0) 14 (100) 0 Massive transfusion* 12 (92) 1 (8) 6 (43) 7 (50) 1 Sepsis 3 (23) 10 (77) 4 (29) 10 (71) 0 ECMO/ECLS* 4 (31) 9 (69) 0 (0) 14 (100) 0 TPE 3 (23) 10 (77) 0 (0) 14 (100) 0 Total 149 (60) 98 (40) 78 (29) 180 (68) 8 (3) Critical Care Protocols HCCs (n = 13) LCCs (n = 14) Present, Effective, Successful Absent or Ineffective Present, Effective, Successful Absent or Ineffective Unknown or Blank Response n (%) n (%) n (%) n (%) n (%) Standard ventilator management 3 (23) 10 (77) 4 (29) 8 (57) 2 Ventilator liberation 11 (85) 2 (15) 7 (50) 7 (50) 0 Respiratory adjunctive therapies 9 (69) 4 (31) 4 (29) 9 (64) 1 Sedation 10 (77) 3 (23) 8 (57) 6 (43) 0 Neuro-muscular paralysis 4 (31) 9 (69) 2 (14) 11 (79) 1 Sleep 5 (38) 8 (62) 3 (21) 11 (79) 0 Delirium management 5 (38) 8 (62) 6 (43) 8 (57) 0 Tight glycemic control* 13 (100) 0 (0) 4 (29) 8 (57) 2 Mobility* 11 (85) 2 (15) 1 (7) 13 (93) 0 Nutrition* 11 (85) 2 (15) 5 (36) 9 (64) 0 DVT prophylaxis 11 (85) 2 (15) 9 (64) 5 (36) 0 GI prophylaxis 9 (69) 4 (31) 8 (57) 6 (43) 0 Therapeutic hypothermia* 12 (92) 1 (8) 6 (43) 8 (57) 0 Euthermia* 7 (54) 6 (46) 1 (7) 12 (86) 1 TBI or brain injury management 6 (46) 7 (54) 0 (0) 14 (100) 0 Massive transfusion* 12 (92) 1 (8) 6 (43) 7 (50) 1 Sepsis 3 (23) 10 (77) 4 (29) 10 (71) 0 ECMO/ECLS* 4 (31) 9 (69) 0 (0) 14 (100) 0 TPE 3 (23) 10 (77) 0 (0) 14 (100) 0 Total 149 (60) 98 (40) 78 (29) 180 (68) 8 (3) Bold and * indicates p < 0.05 for comparison of protocol presence at HCCS vs. LCCs. DVT, deep vein thrombosis; ECLS, extracorporeal life support; ECMO, extracorporeal membrane oxygenation; TBI, traumatic brain injury; GI, gastrointestinal; TPE, therapeutic plasma exchange. Overall, ICU mortality rates across the MHS are low. About 67% of respondents/services reported mortality rates <5% per year, 11% (three respondents) reported rates of 5–10%, one respondent reported a rate of 11–15%, and one reported a rate of 16–20% (Table V). The two services that reported the highest mortality rates were both LCCs with mixed ICUs. Other outcomes such as ICU length of stay (LOS, 3–4 d), ventilator-associated pneumonia (<1/1000) and central line associated blood stream infection (CLABSI, <1/1000) rates were reported as being very low and did not differ between HCCs and LCCs, although one HCC service reported a higher CLABSI rate of 16–20/1000 over the past year. We investigated how critical care services are organized and structured in the MHS. All of LCC and HCC respondents have a physician medical director and head nurse. Seventy-seven percent of HCCs vs. 50% of LCCs reported regularly scheduled meetings between the medical director and the head nurse. Hospital Critical Care Committees were reported as active at all HCCs and only 71% of LCCs. We investigated how often MHS critical care services transfer patients to a higher level of care. During the 3 mo before the survey, HCCs compared with LCCs transferred notably fewer total patients to higher levels of civilian care (3 vs. 58) and military care (0 vs. 9). DISCUSSION Within the MHS, critical care services clearly differ between HCCs and LCCs. HCCs have more service specific ICUs and were more likely to report on behalf of a service rather than a unit or physical location. Structure and process of care are important determinants of patient outcome in the ICU.8 Although controversy exists regarding the benefits of closed vs. open critical care delivery models,8 military researchers have suggested that intensivist-led, high performing, multidisciplinary critical care teams improve outcomes in a deployed ICU setting.9,10 In this study, we found that HCCs were more likely to have closed vs. open ICU models, but that all were managed by a medical director and head nurse. HCCs were more likely to have a critical care committee than were LCCs. The majority of all MHS respondents were unable to provide APACHE II scores and all reported low-mortality rates except for two LCCs. While lack of illness severity reporting, or availability for MHS critical care services, makes it difficult to compare, overall, these data suggest that patients in MHS ICUs are less ill than patients admitted to civilian ICUs where the average admission APACHE II score is 19, and mortality rates average 9%.7 There were two LCCs that reported mortality rates higher than other respondents. Both reported their primary ICU as “cardiac” and did not provide APACHE II scores. The service that reported 11–15% mortality also reported the highest number of patient transfers to a higher level of care (50 patients) during the 6 mo prior to survey. The respondent with the highest mortality rate (16–20%) reported no unique characteristics. There seems to be notable differences in resource availability between HCCs and LCCs. Bedside nurses appear to be more available in the LCCs during MDR than they are at HCCs. Also, there appears to be more availability of ancillary services to support MDR in the LCCs. Increased availability of multidisciplinary care teams has been associated with decreased mortality rates and better outcomes, potentially mitigating the reduced availability of critical care trained physicians at LCCs in the MHS.5 The number of critical care protocols available can be a measure of how services have institutionalized common processes and established clinical practice guidelines. HCCs reported more protocols than LCCs and subjectively found them to be more effective than LCCs. However, human resource availabilities at LCCs may also balance the increased protocol availability and their perceived effectiveness at HCCs in terms of reducing mortality.7 HCCs were more likely to use checklists during MDR than LCCs, but it was highly physician dependent. This highlights that some important critical care processes in MHS ICUs may be personality dependent vice standardized. A recent study has shown that when a checklist was encouraged but not mandatory, the process had no impact on outcomes.12 We sought to better understand service capabilities by asking services for data about the number of patients transferred to other facilities. Although we could not calculate transfer rates as part of this study, the apparent difference in the volume of patient transfers between HCCs and LCCs is expected and is likely related to less availability of sub-specialty expertise at LCCs. This study is the first to successfully describe MHS critical care services and it highlights significant differences between organizational structure and resourcing between high and low capacity MTFs. Previous studies of MHS critical care services, such as the Structural Characteristics of DOD Intensive Care Units study conducted by Lockheed Martin in the fall of 2010, were unable to draw accurate or meaningful conclusions about MHS critical care services due to inaccuracies of administrative data (personal correspondence of JCP with MHS Clinical Quality Management Report on Intensive Care Units: Influence of Organizational Structure on Patient Outcomes, 2010). The most significant limitation of this study was the nature of its data reporting. Because there is no centralized repository for critical care related data, the study relied heavily on respondents identifying necessary information from local data sources and personal experience. In many cases, data sources included paper logbooks (personal communication of the PI, JCP, with POCs). Many respondents were unable to provide all of the information requested. Consequently, there was significant variability in the reporting of data to this survey making our findings only descriptive: definitive conclusions about critical care and its delivery in the MHS cannot be made based on this study. This limitation is, perhaps, the most important finding of this study. Across the MHS, information necessary to make important resourcing and process improvement decisions is not collected or is not easily accessible to clinical leadership. Inconsistent situational awareness of demographic and outcome data, as well the apparently high variability of critical care processes, personnel, and leadership makes it difficult to improve the quality and safety of patient care in ICUs across the MHS. This type of knowledge and understanding about structure, process, and outcomes has been demonstrated to impact patient outcome in critical and trauma care.7,11 Information about these additional aspects of MHS critical care (i.e., local data collection and review processes, personnel and leadership continuity) should be included in future studies. The Joint Trauma System has demonstrated through intentional monitoring of care processes and patient outcomes in combination with regular review of these data and discussion of opportunities to improve the ability to modify old processes or introduce new, more effective ones. Consequently, it facilitates system wide changes that result in improved patient outcomes over relatively short periods of time.11 This is the first study to describe the care models, patient populations, and clinical practices of MHS critical care services. Despite the study’s limitations, important information can be gleaned about how the MHS delivers critical care. First, the MHS does not have a centralized or consistent approach to critical care services across the enterprise. Second, demographic and outcomes data for critical care patients are not consistently available or easily accessible to clinical leadership in critical care. Third, many critical care processes or resources supported by evidenced-based literature are not consistently implemented or available across the enterprise. Given the significance of critical care to hospital costs, its impact on patient outcomes, and the likely association of critical care exposure to wartime skills retention, it is imperative that the MHS work to better understand these services across the enterprise in order to improve process and outcomes for its beneficiaries. This need is particularly important to combat casualty care during the interim between wars.13 Table V. Reported ICU Mortality Rates for HCCs and LCCs Mortality Rate Mortality Rate (%) HCC Services (n = 13) LCC Services (n = 14) Total <5 9 10 19 5–10 3 0 3 11–15 0 1 1 16–20 0 1 1 Unknown 1 2 3 Mortality Rate Mortality Rate (%) HCC Services (n = 13) LCC Services (n = 14) Total <5 9 10 19 5–10 3 0 3 11–15 0 1 1 16–20 0 1 1 Unknown 1 2 3 HCCs, high capacity center; LCCs, low capacity center. Table V. Reported ICU Mortality Rates for HCCs and LCCs Mortality Rate Mortality Rate (%) HCC Services (n = 13) LCC Services (n = 14) Total <5 9 10 19 5–10 3 0 3 11–15 0 1 1 16–20 0 1 1 Unknown 1 2 3 Mortality Rate Mortality Rate (%) HCC Services (n = 13) LCC Services (n = 14) Total <5 9 10 19 5–10 3 0 3 11–15 0 1 1 16–20 0 1 1 Unknown 1 2 3 HCCs, high capacity center; LCCs, low capacity center. Authors’ Contributions JJN participated in data analysis, wrote the draft manuscript, and participated in multiple pre-submission revisions of the manuscript. CJC helped conceive of the study idea, study protocol, and survey items in addition to analyzing data and being a primary writer and editor of the manuscript. CAM helped conceive of the study idea and study protocol, in addition to analyzing data and editing the manuscript. EAM-S helped conceive of the study idea and study protocol, in addition to analyzing data and editing the manuscript. FB completed the first analysis of the data, produced the initial data tables and abstract for presentation and this manuscript and assisted with editing the manuscript. AWB helped develop the study protocol, analyze data and edit the manuscript. KD helped conceive of the study idea and study protocol, in addition to analyzing data and editing the manuscript. JKA performed statistical support for the study protocol, statistical analysis of the data, and helped edit the manuscript. KKC helped conceive of the study idea and study protocol, in addition to analyzing data and editing the manuscript. MSM helped conceive of the study idea and study protocol, in addition to analyzing data and editing the manuscript. JCP was the principal investigator and originator for the study concept and protocol. He helped analyze the data, write and edit the manuscript, and gave final approval for its submission. Acknowledgements Mrs. Nicole Caldwell, U.S. Army Institute of Surgical Research, Fort Sam Houston, TX, USA, for her support with maintaining research and regulatory files. All of the following for contributing to the coordination and survey responses for this project: MAJ Craig Ainsworth MD, William Beaumont Army Medical Center, El Paso, TX, USA. MAJ Alain Abellada MD, Blanchfield Army Community Hospital, Fort Campbell, KY, USA. COL Stephen Silvey MD, MAJ Scott Trexler MD, LTC(P) David Bell MD, Houmayoun Ahmadian DO, and Col(ret) Jeffrey McNeil MD, Brooke Army Medical Center, JBSA Fort Sam Houston, TX, USA. COL Harold Thomas MD, Darnell Army Medical Center, Fort Hood, TX, USA. Dr. Michael Cole MD, Fort Belvoir Community Hospital, Fort Belvoir, VA, USA. LTC Sean Reilly MD, Landstuhl Regional Medical Center, Landstuhl, Germany. LTC Larry Linville MSN, General Leonard Wood Army Community Hospital, Fort Leonard Wood, MO, USA. Dr. Bruce Lovins MD, Martin Army Community Hospital, Fort Benning, GA, USA. LTC Jessica Bunin MD, MAJ Matthew Aboudara MD, Tripler Army Medical Center, Honolulu, HI, USA. COL Stewart McCarver MD, Walter Reed National Military Medical Center, Bethesda, MD, USA. MAJ Douglas Powell MD, Womack Army Medical Center, Fort Bragg, NC, USA. Col Brian Delmonaco MD, Mike O’Callaghan Federal Medical Center, Nellis AFB, NV, USA. Maj John Untisz DO, Eglin Air Force Base Hospital, Eglin AFB, FL, USA. Maj Tokunbo Matthews MD, Joint Base Elmendorf-Richardson Hospital, Anchorage, AK, USA. Ltc Christopher Dennis DO, David Grant US Air Force Medical Center, Travis AFB, CA, USA. MAJ Dara Regn MD, Wright-Patterson Medical Center, Dayton, OH, USA. Dr. Shawn French MD, Keesler Medical Center, Keesler AFB, MS. LCDR Thuy Lin MD, Naval Medical Center Portsmouth, Portsmouth, VA, USA. LCDR Ryan Maves MD, Naval Medical Center San Diego, San Diego, CA, USA. LCDR Russell Miller MD, Naval Hospital Camp Pendleton, Camp Pendleton, CA, USA. Dr. James Prahl MD, Naval Hospital Guam, Tutuhan, Guam. Supplementary data Supplementary data are available at Military Medicine online. References 1 Halpern NA , Pastores SM : Critical care medicine in the United States 2000–2005: an analysis of bed numbers, occupancy rates, payer mix, and costs . Crit Care Med 2010 ; 38 : 65 – 71 . Google Scholar CrossRef Search ADS PubMed 2 Barnato AE , Kahn JM , Rubenfeld GD , McCauley K , Fontaine D , Frassica JJ , et al. : Prioritizing the organization and management of intensive care services in the United States: the PrOMIS Conference. 2007 . pp. 1003–11. 3 Harvey MA , Penoyer DA , Jastremski C : “Building Teamwork to Improve Outcomes.”. In: Textbook of Critical Care: Edition , pp 1589 – 94 . Edited by Vincent JL , Abraham E , Kochanek P , Moore F , Fink M . Philadelphia , Saunders , 2011 . Print. Google Scholar CrossRef Search ADS 4 Chalfin DB , Cohen IL , Lambrinos J : The economics and cost-effectiveness of critical care medicine . Intensive Care Med 1995 ; 21 : 952 – 61 . Google Scholar CrossRef Search ADS PubMed 5 Levy MM , Dellinger RP , Townsend SR , Linde-Zwirble WT , Marshall JC , Bion J , et al. : The Surviving Sepsis Campaign: results of an international guideline-based performance improvement program targeting severe sepsis . Crit Care Med 2010 ; 38 : 367 – 74 . Google Scholar CrossRef Search ADS PubMed 6 The United Hospital Fund , “Critical Care Leadership Network”. Available at https://uhfnyc.org/initiatives/quality_improvement/critical-care-leadership-network, accessed 26 October 2017 . 7 Checkley W , Martin GS , Brown SM , Chang SY , Dabbagh O , Fremont RD , et al. : Structure, process, and annual ICU mortality across 69 centers: United States Critical Illness and Injury Trials Group Critical Illness Outcomes Study . Crit Care Med 2014 ; 42 : 344 – 56 . Google Scholar CrossRef Search ADS PubMed 8 Weled BJ , Adzhigirey LA , Hodgman TM , Brilli RJ , Spevetz A , Kline AM , et al. : Critical care delivery: the importance of process of care and ICU structure to improved outcomes: an update from the American College of critical care medicine task force on models of critical care . Crit Care Med 2015 ; 43 : 1520 – 5 . Google Scholar CrossRef Search ADS PubMed 9 Lettieri CJ , Shah AA , Greenburg DL : An intensivist-directed intensive care unit improves clinical outcomes in a combat zone . Crit Care Med 2009 ; 37 : 1256 – 60 . Google Scholar CrossRef Search ADS PubMed 10 Grathwohl KW , Venticinque SG : Organizational characteristics of the austere intensive care unit: the evolution of military trauma and critical care medicine; applications for civilian medical care systems . Crit Care Med 2008 ; 36 : S275 – 83 . Google Scholar CrossRef Search ADS PubMed 11 Palm K , Apodaca A , Spencer D , Costanzo G , Bailey J , Fortuna G , et al. : Evaluation of military trauma system practices related to complications after injury . J Trauma Acute Care Surg 2012 ; 73 : S465 – 71 . Google Scholar CrossRef Search ADS PubMed 12 Urbach DR , Govindarajan A , Saskin R , Wilton AS , Baxter NN : Introduction of surgical safety checklists in Ontario, Canada . N Engl J Med 2014 ; 370 : 1029 – 38 . Google Scholar CrossRef Search ADS PubMed 13 Rasmussen TE , Gross KR , Baer DG : Where do we go from here? J Trauma Acute Care Surg 2013 ; 75 : S105 – 6 . Google Scholar CrossRef Search ADS PubMed Author notes The views expressed are those of the author(s) and do not reflect the official policy or position of the U.S. Army Medical Department, Department of the Army, Department of the Air Force, Department of the Navy, Department of Defense, or the U.S. Government. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US.

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Military MedicineOxford University Press

Published: Mar 29, 2018

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