Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models.

OBJECTIVE: The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the stratification of trauma injury severity across various body regions using clinical text and structured electronic health records (EHRs) data. MATERIALS AND METHODS: Our study utilized clinical documents and structured EHR variables linked with the trauma registry data to create 2 machine learning models with different approaches to representing text. The first one fuses concept unique identifiers (CUIs) extracted from free text with structured EHR variables, while the second one integrates free text with structured EHR variables. Temporal validation was undertaken to ensure the models' temporal generalizability. Additionally, analyses to assess the variable importance were conducted. RESULTS: Both models demonstrated impressive performance in categorizing leg injuries, achieving high accuracy with macro-F1 scores of over 0.8. Additionally, they showed considerable accuracy, with macro-F1 scores exceeding or near 0.7, in assessing injuries in the areas of the chest and head. We showed in our variable importance analysis that the most important features in the model have strong face validity in determining clinically relevant trauma injuries. DISCUSSION: The CUI-based model achieves comparable performance, if not higher, compared to the free-text-based model, with reduced complexity. Furthermore, integrating structured EHR data improves performance, particularly when the text modalities are insufficiently indicative. CONCLUSIONS: Our multi-modal, multiclass models can provide accurate stratification of trauma injury severity and clinically relevant interpretations.

Experiential Learning of Interdisciplinary Care Skills in Surgery Assessed From Student Reflections.

INTRODUCTION: Interdisciplinary healthcare collaboration improves patient outcomes, increases workplace satisfaction, and reduces costs. Our medical school utilizes an experiential learning tool for teaching interprofessionalism known as the Longitudinal Patient Project (LPP). Medical students are directed to identify a surgical patient to establish continuity with by observing them throughout preoperative, intraoperative, and postoperative periods, and follow-up with the patient after their procedure. Students then write reflections on their LPP experience. This study examines the LPP as an interprofessionalism teaching tool using qualitative analysis of student reflections. METHODS: NVivo 12 was used to code reflections. One researcher coded reflections for subject, depth, temporality, and confidence. Depth was assessed using Mezirow's Critical Reflection Theory, with students receiving titles of "content," "process," or "premise" reflectors based on the deepest level of reflection exhibited. Confidence was assessed by labeling reflective statements as "concrete" or "verbal." Data were coded by a second researcher for validation. Consensus was reached, the remainder of the dataset was updated to reflect codebook changes, and trends were assessed. RESULTS: Inter-rater agreement was 83%. All students achieved "content" level reflection. Ninety-seven percent of students reached "process" reflection. Ninety-three percent of students reached "premise" reflection. Students provided more concrete indicators of knowledge gained from the LPP than from prior experiences. Subjects included communication, team dynamics, patient impact, and student experience. Increased depth and breadth of reflection on communication and team dynamics were observed from the LPP. CONCLUSIONS: The LPP illustrates the importance of interdisciplinary care in surgery. Future iterations should emphasize the impact on patients and their families.

Which hospital-acquired conditions matter the most in trauma? An evidence-based approach for prioritizing trauma program improvement.

BACKGROUND: Prevention of hospital-acquired conditions (HACs) is a focus of trauma center quality improvement. The relative contributions of various HACs to postinjury hospital outcomes are unclear. We sought to quantify and compare the impacts of six HACs on early clinical outcomes and resource utilization in hospitalized trauma patients. METHODS: Adult patients from the 2013 to 2016 American College of Surgeons Trauma Quality Improvement Program Participant Use Data Files who required 5 days or longer of hospitalization and had an Injury Severity Score of 9 or greater were included. Multiple imputation with chained equations was used for observations with missing data. The frequencies of six HACs and five adverse outcomes were determined. Multivariable Poisson regression with log link and robust error variance was used to produce relative risk estimates, adjusting for patient-, hospital-, and injury-related factors. Risk-adjusted population attributable fractions estimates were derived for each HAC-outcome pair, with the adjusted population attributable fraction estimate for a given HAC-outcome pair representing the estimated percentage decrease in adverse outcome that would be expected if exposure to the HAC had been prevented. RESULTS: A total of 529,856 patients requiring 5 days or longer of hospitalization were included. The incidences of HACs were as follows: pneumonia, 5.2%; urinary tract infection, 3.4%; venous thromboembolism, 3.3%; surgical site infection, 1.3%; pressure ulcer, 1.3%; and central line-associated blood stream infection, 0.2%. Pneumonia demonstrated the strongest association with in-hospital outcomes and resource utilization. Prevention of pneumonia in our cohort would have resulted in estimated reductions of the following: 22.1% for end organ dysfunction, 7.8% for mortality, 8.7% for prolonged hospitalization, 7.1% for prolonged intensive care unit stay, and 6.8% for need for mechanical ventilation. The impact of other HACs was comparatively small. CONCLUSION: We describe a method for comparing the contributions of HACs to outcomes of hospitalized trauma patients. Our findings suggest that trauma program improvement efforts should prioritize pneumonia prevention. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level IV.

Disaster Research/Evaluation Frameworks, Part 1: An Overview-RETRACTED.

The goals of conducting disaster research are to obtain information to: (1) decrease the human, environmental, and economic losses; (2) decrease morbidity; (3) decrease pain and suffering; and (4) enhance the recovery of the affected population. Two principal, but inter-related, branches of disaster research are: (1) Epidemiological; and (2) Interventional. In response to the need for the discipline of disaster health to build its science on data that are generalizable and comparable, a set of five Frameworks have been developed to structure the information and research of the health aspects of disasters: (1) Conceptual; (2) Longitudinal; (3) Transectional Societal; (4) Relief-Recovery; and (5) Risk-Reduction. These Frameworks provide a standardized format for studying and comparing the epidemiology of disasters as well as evaluating the interventions (responses) provided prior to, during, and following a disaster, especially as they relate to the health status of the people affected or at-risk. Critical to all five Frameworks is the inclusion of standardized definitions of the terms used to describe factors that lead to and affect the occurrence and severity of a disaster. The Conceptual Framework describes the progression of a hazard that becomes an event, which causes structural damage and a decrease or loss of function (functional damage), that, in turn, produces needs that lead to a disaster. The Longitudinal Framework describes this chronological progression as phases in order of their appearance in time, even though some of them occur concurrently. In order to study and compare the effects of an event on the complex amalgam that constitutes a society, the essential functions of a society have been deconstructed into 13 Basic Societal Systems that comprise the Transectional Societal Framework. These diverse, but inter-related Basic Societal Systems interface with each other through a 14th system called Coordination and Control. Epidemiological research studies the relationships and occurrences that influence and result from a disaster. Interventional research involves the evaluation of interventions, whether they are directed at relief, recovery, hazard mitigation, capacity building, or performance. The Relief-Recovery and Risk-Reduction Frameworks are based on a Disaster Logic Model. The Relief-Recovery Framework provides the structure necessary to systematically evaluate specific interventions provided during the Relief and Recovery phases of a disaster. The Risk-Reduction Framework details the processes involved in mitigating the risk that a hazard will produce a destructive event and/or that capacity building will augment the resilience of a community to the consequences of such an event. It incorporates a cascade of risks that lead from the presence of a hazard to the development of a disaster. Risk is described as the likelihood that each of the steps leading from a hazard to a disaster will take place; it also includes the probable consequences of the occurrence of each of the elements in the Conceptual Framework. The Conceptual, Longitudinal, and Transectional Societal Frameworks are useful in epidemiological research, i.e., the study of the incidence of, and factors influencing events and disasters. The Relief-Recovery and Risk-Reduction Frameworks are added to the Conceptual, Longitudinal, and Transectional Societal Frameworks for conducting and reporting of interventional research/evaluations. Examples of the application of these Frameworks are provided.

Simulation-Based Medical Education: Development of an Assessment Tool for Novice Use.

BACKGROUND: Simulation-based medical education, an educational model in which students engage in simulated patient scenarios, improves performance. However, assessment tools including the Oxford Non-Technical Skills (NOTECHS) scale require expert assessors. We modified this tool for novice use. METHODS: Medical students participated in 5 nontechnical simulations. The NOTECHS scale was modified to allow for novice evaluation. Three novices and 2 experts assessed performance, with intraclass correlation used to assess validity. RESULTS: Twenty-two learners participated in the simulations. Novice reviewers had moderate to excellent correlation among evaluations (0.66 < intraclass correlation coefficients [ICC] < 0.95). Novice and expert reviewers had moderate to good correlation among evaluations (0.51 < ICC < 0.88). DISCUSSION: The modified NOTECHS scales can be utilized by novices to evaluate simulation performance. Novice assessment correlates with expert review. These tools may encourage the use of simulation-based medical education.

"Rhythm of the Night": Medical student reflections on overnight trauma call.

INTRODUCTION: On their surgical clerkship, students reflected on their experience on a traditional overnight call. We explored whether perceived learning experiences differ between students who identify surgical specialties as their career compared to those who do not. METHODS: Medical students participated in traditional call at a Level 1 Trauma Center and submitted guided reflections. Reflections were coded using thematic analysis. We used Epistemic Network Analysis to compare codes based on matched specialty. RESULTS: 418 students submitted reflections between 2016 and 2019.95% learned something on call they could not have during daily services. Students who matched in surgical specialties connected more call learning experiences to formation of student agency through individualized teaching experiences and awareness of stamina. CONCLUSIONS: Most students found participation in overnight call valuable, and professed increased awareness of their skills and future residency duties. Results indicate students who matched in surgical specialties had more formative experiences on call.

Comparison and interpretability of machine learning models to predict severity of chest injury.

OBJECTIVE: Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at nontrauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting. MATERIALS AND METHODS: A level I trauma center was queried for patients presenting after injury between 2014 and 2018. Prediction modeling was performed to classify severe chest injury using a reference dataset labeled by certified registrars. Clinical documents from trauma encounters were processed into concept unique identifiers for inputs to ML models: logistic regression with elastic net (EN) regularization, extreme gradient boosted (XGB) machines, and convolutional neural networks (CNN). The optimal model was identified by examining predictive and face validity metrics using global explanations. RESULTS: Of 8952 encounters, 542 (6.1%) had a severe chest injury. CNN and EN had the highest discrimination, with an area under the receiver operating characteristic curve of 0.93 and calibration slopes between 0.88 and 0.97. CNN had better performance across risk thresholds with fewer discordant cases. Examination of global explanations demonstrated the CNN model had better face validity, with top features including "contusion of lung" and "hemopneumothorax." DISCUSSION: The CNN model featured optimal discrimination, calibration, and clinically relevant features selected. CONCLUSION: NLP and ML methods to populate trauma registries for quality analyses are feasible.

Comparison of the Perspectives of Medical Students and Residents on the Surgery Learning Environment.

BACKGROUND: The learning environment plays a critical role in learners' satisfaction and outcomes. However, we often lack insight into learners' perceptions and assessments of these environments. It can be difficult to discern learners' expectations, making their input critical. When medical students and surgery residents are asked to evaluate their teachers, what do they focus on? MATERIALS AND METHODS: Open-ended comments from medical students' evaluations of residents and attending surgeons and from residents' evaluations of attendings during the 2016-2017 academic year were analyzed. Content analysis was used, and codes derived from the data. A matrix of theme by learner role was created to distinguish differences between medical student and resident learners. Subthemes were grouped based on similarity into high-order themes. RESULTS: Two overarching themes were Creating a positive environment for learning by modeling professional behaviors and Intentionally engaging learners in training and educational opportunities. Medical students and residents made similar comments for the subthemes of appropriate demeanor, tone and dialog, respect, effective direct instruction, feedback, debriefing, giving appropriate levels of autonomy, and their expectations as team members on a service. Differences existed in the subthemes of punctuality, using evidence, clinical knowledge, efficiency, direct interactions with patients, learning outcomes, and career decisions. CONCLUSIONS: Faculty development efforts should target professional communication, execution of teaching skills, and relationships among surgeons, other providers, and patients. Attendings should make efforts to discuss their approach to clinical decision making and patient interactions and help residents and medical students voice their opinions and questions through trusting adult learner-teacher relationships.

Prediction of severe chest injury using natural language processing from the electronic health record.

INTRODUCTION: Trauma injury severity scores are currently calculated retrospectively from the electronic health record (EHR) using manual annotation by certified trauma coders. Natural language processing (NLP) of clinical documents in the EHR may enable automated injury scoring. We hypothesize that NLP with machine learning can discriminate between cases of severe and non-severe injury to the thorax after trauma. METHODS: Clinical documents from a trauma center were examined between 2014 and 2018. Severe chest injury was defined as a thorax abbreviated injury score (AIS) >2 and served as the reference standard for supervised learning. Free text unigrams and concept unique identifiers (CUIs) from the Unified Medical Language Systems (UMLS) were extracted from clinical documents collected at one hour, four hours, and eight hours after patient arrival to the emergency department. Logistic regression models with elastic net regularization were tuned to maximize area under the receiver operating characteristic curve (AUROC) using 10-fold cross-validation on the training dataset (80%) and tested on a hold-out 20% dataset. RESULTS: There were 6,891 traumas that met inclusion criteria. The complete data corpus consisted of 473,694 documents. Models trained using the first hour of data had a mean AUROC of 0.88 (95%CI [0.86, 0.89]); model discrimination and reclassification from the first hour significantly improved after eight hours with a mean AUROC of 0.94 (95%CI [0.93, 0.95]). Performance of models using CUIs were similar to unigrams (p>0.05). Models demonstrated excellent clinical face validity. CONCLUSIONS: Both CUIs and unigrams demonstrated excellent discrimination in predicting severity of chest injury using the first eight hours of clinical documents. Our model demonstrates that automated anatomical injury scoring is feasible and may be used for aggregation of data for trauma research and quality programs.

Safety in Allowing Residents to Independently Perform Appendectomy: A Retrospective Review.

BACKGROUND: In an era of competency-based education and concern about graduating resident readiness for practice, early resident autonomy and the ability to safely teach junior residents is becoming increasingly important. In this study, we aimed to understand the effect of "teaching resident" (2 residents operating under the supervision of an attending physician) appendectomy cases on outcomes. STUDY DESIGN: We performed a single-center retrospective review of 928 patients who underwent appendectomy within the University of Wisconsin hospital system, from October 2014 to December 2017. We examined how 2 residents (compared with 1 resident with an attending) attempting a case affected operation time, surgical site infection (SSI) rate, conversion to open rate, postoperative CT scanning, and readmission rate, while controlling for sex, age, American Society of Anesthesiologists (ASA) class, BMI, previous lower abdominal surgery, acuity, perforation, and presence of a junior attending. RESULTS: We identified 597 1-resident cases and 331 2-resident or "teaching resident" cases. We performed multiple logistic regression to assess teaching resident cases as a predictor of postoperative outcomes. There were no significant differences in postoperative surgical site infection (superficial or organ space) odds ratio (OR) = 0.83 (95% CI, 0.47, 1.45); p = 0.51, conversion to open OR = 1.10 (95% CI, 0.46, 2.60); p = 0.84, postoperative CT scanning OR = 0.82 (95% CI, 0.48, 1.35); p = 0.42, or readmission within 30 days OR = 0.76 (95% CI, 0.40, 1.44); p = 0.40. However, teaching resident operative times were more likely to be classified as prolonged OR = 1.44 (95% CI, 1.03, 2.01); p = 0.03. CONCLUSIONS: Senior surgical trainees can safely supervise more junior trainees performing appendectomy procedures, and training programs should encourage faculty to allow residents to not only manage operative appendicitis as independently as possible, but to supervise junior residents in the intraoperative management of appendicitis.

Proficiency development for graduating medical students, using skills-level-appropriate mastery learning versus traditional learning for chest tube placement: Assessing anxiety, confidence, and performance.

BACKGROUND: Mastery learning is an effective educational tool to assess basic procedural skill proficiency and may also be beneficial for more complex skills along the continuum of surgical training. In addition, anxiety and confidence have effects on cognitive and decision-making performance, both in educational and clinical settings. This study evaluates anxiety and confidence in a skills-level-appropriate mastery learning module for chest tube insertion in graduating medical students. METHODS: A 2-week intern preparatory course was held 2 consecutive years, with 10 and 14 students, respectively. Learners completed a pretest on day 1, didactic session and supervised deliberate practice followed by a Posttest on day 4, and a retention test on day 10. Year one used a traditional educational methodology, and year two provided for remediation as per mastery learning methodology. The chest tube scoring checklist was validated by faculty trauma surgeons to reflect an intern-appropriate skills level. Before and after each test, learners reported state anxiety. Immediately after each test, learners also completed a confidence scale. RESULTS: No learners in either year achieved mastery on the pretest. A total of 40% of the learners achieved the mastery standard on the posttest in year one. All (100%) of the learners achieved the mastery standard after the posttest in year two. Overall, after state anxiety decreased significantly in both years, confidence increased significantly in year two. CONCLUSION: A skills-level-appropriate mastery learning module resulted in higher performance and increased confidence compared with a traditional education model for chest tube placement for incoming surgical interns.

Patient-Centered Outcome Spectrum: An Evidence-based Framework to Aid in Shared Decision-making.

OBJECTIVE: Our objective was to develop an alternate construct for reporting anticipated outcomes after emergency general surgery (EGS) that presents risk in terms of a composite measure. BACKGROUND: Currently available prediction tools generate risk outputs for discrete as opposed to composite measures of postoperative outcomes. A construct to synthesize multiple discrete estimates into a global understanding of a patient's likely postoperative health status is lacking and could augment shared decision-making conversations. METHODS: Using the 2012 to 2014 American College of Surgeons National Surgical Quality Improvement Program Participant Use File, we developed the Patient-Centered Outcomes Spectrum (PCOS) for patients ≥65 years old who underwent an EGS operation. The PCOS defines 3 exclusive types of global outcomes (good, intermediate, and bad outcomes) and allows patients to be prospectively stratified by both their EGS diagnosis and preoperative surgical risk profile. RESULTS: Of the patients in our study population, 13,330 (46.4%) experienced a 30-day postoperative course considered a good outcome. Conversely, 3791 (13.2%) of study patients experienced a bad outcome. The remainder of patients (11,617; 40.4%) were classified as experiencing an intermediate outcome. The incidence of good, intermediate, and bad outcomes was 69.7%, 28.2%, and 2.1% for low-risk patients, and 22.0%, 48.9%, and 29.1% for high-risk patients. Diagnosis-specific PCOS constructs are also provided. CONCLUSIONS: Consistent with the goals of shared decision-making, the PCOS provides an evidence-based construct based upon a composite outcome measure for patients and providers as they weigh the risks of undergoing EGS.

Number of rib fractures thresholds independently predict worse outcomes in older patients with blunt trauma.

BACKGROUND: There have been conflicting reports regarding whether the number of rib fractures sustained in blunt trauma is associated independently with worse patient outcomes. We sought to investigate this risk-adjusted relationship among the lesser-studied population of older adults. METHODS: A retrospective review of the National Trauma Data Bank was performed for patients with blunt trauma who were ≥65 years old and had rib fractures between 2009 and 2012 (N = 67,695). Control data were collected for age, sex, injury severity score, injury mechanism, 24 comorbidities, and number of rib fractures. Outcome data included hospital mortality, hospital and intensive care unit durations of stay, duration of mechanical ventilation, and the occurrence of pneumonia. Multiple logistic and linear regression analyses were performed. RESULTS: Sustaining ≥5 rib fractures was associated with increased intensive care unit admission (odds ratio: 1.14, P < .001) and hospital duration of stay (relative duration: 105%, P < .001). Sustaining ≥7 rib fractures was associated with an increased incidence of pneumonia (odds ratio: 1.32, P < .001) and intensive care unit duration of stay (relative duration: 122%, P < .001). Sustaining ≥8 rib fractures was associated with increased mortality (odds ratio: 1.51, P < .001) and duration of mechanical ventilation (relative duration: 117%, P < .001). CONCLUSION: In older patients with trauma, sustaining at least 5 rib fractures is a significant predictor of worse outcomes independent of patient characteristics, comorbidities, and trauma burden.

Nonoperative Management Is as Effective as Immediate Splenectomy for Adult Patients with High-Grade Blunt Splenic Injury.

BACKGROUND: The comparative effectiveness of nonoperative management (NOM) vs immediate splenectomy (IS) for hemodynamically stable adult patients with grade IV or V blunt splenic injury (BSI) has not been clearly established in the literature. STUDY DESIGN: We performed a retrospective analysis of adult patients, from the 2013 to 2014 American College of Surgeons Trauma Quality Improvement Program (TQIP) Participant Use Data Files, who sustained grade IV or V BSI. Outcomes after IS vs attempted NOM were compared using propensity score analysis in order to adjust for patient- and injury-related variables. RESULTS: Nonoperative management was attempted in 1,489 (52.2%) of 2,746 patients who sustained grade IV or V BSI. Propensity matching techniques resulted in a cohort of 758 IS and NOM patients who were well matched for all known patient- and injury-related variables. In-hospital mortality was not different between the IS and NOM patients (11.5% vs 10.0%, p = 0.33), although IS patients had a higher incidence of infectious complications (21.4% vs 16.9%, p = 0.02). The rate of NOM failure in our sample was 20.1%. Independent predictors of failed NOM included the presence of a bleeding disorder, early blood transfusion requirement, and grade V injury. Splenic artery embolization was associated with a decreased risk of NOM failure. Patients who had failed NOM had a lower in-hospital mortality rate than IS patients (6.4% vs 16.4%, p = 0.004), but required longer hospitalization. CONCLUSIONS: Nonoperative management is as effective as IS for hemodynamically stable adult patients with grade IV or V BSI. The delay in operative intervention that results from failed attempts at NOM does not adversely affect the outcomes of patients who ultimately require splenectomy.

Research and Evaluations of the Health Aspects of Disasters, Part IX: Risk-Reduction Framework.

A disaster is a failure of resilience to an event. Mitigating the risks that a hazard will progress into a destructive event, or increasing the resilience of a society-at-risk, requires careful analysis, planning, and execution. The Disaster Logic Model (DLM) is used to define the value (effects, costs, and outcome(s)), impacts, and benefits of interventions directed at risk reduction. A Risk-Reduction Framework, based on the DLM, details the processes involved in hazard mitigation and/or capacity-building interventions to augment the resilience of a community or to decrease the risk that a secondary event will develop. This Framework provides the structure to systematically undertake and evaluate risk-reduction interventions. It applies to all interventions aimed at hazard mitigation and/or increasing the absorbing, buffering, or response capacities of a community-at-risk for a primary or secondary event that could result in a disaster. The Framework utilizes the structure provided by the DLM and consists of 14 steps: (1) hazards and risks identification; (2) historical perspectives and predictions; (3) selection of hazard(s) to address; (4) selection of appropriate indicators; (5) identification of current resilience standards and benchmarks; (6) assessment of the current resilience status; (7) identification of resilience needs; (8) strategic planning; (9) selection of an appropriate intervention; (10) operational planning; (11) implementation; (12) assessments of outputs; (13) synthesis; and (14) feedback. Each of these steps is a transformation process that is described in detail. Emphasis is placed on the role of Coordination and Control during planning, implementation of risk-reduction/capacity building interventions, and evaluation. Birnbaum ML , Daily EK , O'Rourke AP , Loretti A . Research and evaluations of the health aspects of disasters, part IX: Risk-Reduction Framework. Prehosp Disaster Med. 2016;31(3):309-325.

Research and Evaluations of the Health Aspects of Disasters, Part VIII: Risk, Risk Reduction, Risk Management, and Capacity Building.

There is a cascade of risks associated with a hazard evolving into a disaster that consists of the risk that: (1) a hazard will produce an event; (2) an event will cause structural damage; (3) structural damage will create functional damages and needs; (4) needs will create an emergency (require use of the local response capacity); and (5) the needs will overwhelm the local response capacity and result in a disaster (ie, the need for outside assistance). Each step along the continuum/cascade can be characterized by its probability of occurrence and the probability of possible consequences of its occurrence, and each risk is dependent upon the preceding occurrence in the progression from a hazard to a disaster. Risk-reduction measures are interventions (actions) that can be implemented to: (1) decrease the risk that a hazard will manifest as an event; (2) decrease the amounts of structural and functional damages that will result from the event; and/or (3) increase the ability to cope with the damage and respond to the needs that result from an event. Capacity building increases the level of resilience by augmenting the absorbing and/or buffering and/or response capacities of a community-at-risk. Risks for some hazards vary by the context in which they exist and by the Societal System(s) involved. Birnbaum ML , Loretti A , Daily EK , O'Rourke AP . Research and evaluations of the health aspects of disasters, part VIII: risk, risk reduction, risk management, and capacity building. Prehosp Disaster Med. 2016;31(3):300-308.

Research and Evaluations of the Health Aspects of Disasters, Part VII: The Relief/Recovery Framework.

The principal goal of research relative to disasters is to decrease the risk that a hazard will result in a disaster. Disaster studies pursue two distinct directions: (1) epidemiological (non-interventional); and (2) interventional. Both interventional and non-interventional studies require data/information obtained from assessments of function. Non-interventional studies examine the epidemiology of disasters. Interventional studies evaluate specific interventions/responses in terms of their effectiveness in meeting their respective objectives, their contribution to the overarching goal, other effects created, their respective costs, and the efficiency with which they achieved their objectives. The results of interventional studies should contribute to evidence that will be used to inform the decisions used to define standards of care and best practices for a given setting based on these standards. Interventional studies are based on the Disaster Logic Model (DLM) and are used to change or maintain levels of function (LOFs). Relief and Recovery interventional studies seek to determine the effects, outcomes, impacts, costs, and value of the intervention provided after the onset of a damaging event. The Relief/Recovery Framework provides the structure needed to systematically study the processes involved in providing relief or recovery interventions that result in a new LOF for a given Societal System and/or its component functions. It consists of the following transformational processes (steps): (1) identification of the functional state prior to the onset of the event (pre-event); (2) assessments of the current functional state; (3) comparison of the current functional state with the pre-event state and with the results of the last assessment; (4) needs identification; (5) strategic planning, including establishing the overall strategic goal(s), objectives, and priorities for interventions; (6) identification of options for interventions; (7) selection of the most appropriate intervention(s); (8) operational planning; (9) implementation of the intervention(s); (10) assessments of the effects and changes in LOFs resulting from the intervention(s); (11) determination of the costs of providing the intervention; (12) determination of the current functional status; (13) synthesis of the findings with current evidence to define the benefits and value of the intervention to the affected population; and (14) codification of the findings into new evidence. Each of these steps in the Framework is a production function that facilitates evaluation, and the outputs of the transformation process establish the current state for the next step in the process. The evidence obtained is integrated into augmenting the respective Response Capacities of a community-at-risk. The ultimate impact of enhanced Response Capacity is determined by studying the epidemiology of the next event.

Research and Evaluations of the Health Aspects of Disasters, Part VI: Interventional Research and the Disaster Logic Model.

Disaster-related interventions are actions or responses undertaken during any phase of a disaster to change the current status of an affected community or a Societal System. Interventional disaster research aims to evaluate the results of such interventions in order to develop standards and best practices in Disaster Health that can be applied to disaster risk reduction. Considering interventions as production functions (transformation processes) structures the analyses and cataloguing of interventions/responses that are implemented prior to, during, or following a disaster or other emergency. Since currently it is not possible to do randomized, controlled studies of disasters, in order to validate the derived standards and best practices, the results of the studies must be compared and synthesized with results from other studies (ie, systematic reviews). Such reviews will be facilitated by the selected studies being structured using accepted frameworks. A logic model is a graphic representation of the transformation processes of a program [project] that shows the intended relationships between investments and results. Logic models are used to describe a program and its theory of change, and they provide a method for the analyzing and evaluating interventions. The Disaster Logic Model (DLM) is an adaptation of a logic model used for the evaluation of educational programs and provides the structure required for the analysis of disaster-related interventions. It incorporates a(n): definition of the current functional status of a community or Societal System, identification of needs, definition of goals, selection of objectives, implementation of the intervention(s), and evaluation of the effects, outcomes, costs, and impacts of the interventions. It is useful for determining the value of an intervention and it also provides the structure for analyzing the processes used in providing the intervention according to the Relief/Recovery and Risk-Reduction Frameworks.