Journal articles on the topic 'Critical care medicine Decision making Data processing'
To see the other types of publications on this topic, follow the link: Critical care medicine Decision making Data processing.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Critical care medicine Decision making Data processing.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Eby, D., and J. Woods. "P052: The importance of structured ambulance radio patches during termination of resuscitation calls." CJEM 19, S1 (May 2017): S95. http://dx.doi.org/10.1017/cem.2017.254.
Full textAbstract:
Introduction: Pre-hospital telecommunication (patches) requires a special type of conversation. Receiving and processing correct information is critical when making clinical decisions, such as a termination of resuscitation (ToR). In a study of radio patches, a common patch structure emerged from the data analysis. Use of this standard structure resulted in shorter and less confusing patches. We sought to understand patch structure to be able to target interventions to improve the quality and efficiency of communication needed for critical clinical decisions. Methods: We undertook a retrospective analysis of all ToR patches between physicians and paramedics from 4 paramedic services, recorded by the Ambulance Dispatch Centre between Jan 01-Dec 31, 2014. Four services used Primary Care Paramedics and 1 service also used Advanced Care Paramedics. MP3 patch recording files were anonymized, transcribed, and read multiple times by the authors. Transcripts were coded and analyzed using mixed methods-quantitative descriptive statistics and qualitative thematic framework analysis. Results: The data set was 127 ToR patches-466 pages of transcripts. 116 patches (91.3%) had a standard structure (SS): participant introduction, clinical data presentation, clarification of data, making the decision, exchange of administrative information, and sign off. Paramedics used a mean of 81 words (95CI 74,88) to present the ‘clinical data’. Enough data was presented to meet ToR rule criteria in 52 cases (44.8%). Before making a decision to terminate resuscitation, physicians sought clarification in 100 cases (78.7%). After making the ToR decision, some physicians needed to justify their decision by seeking more data in 17 cases (13.4%). Exchange of non-clinical information (numbers, times, name spellings) took a mean of 200 words (95CI 172,228) and averaged 84 seconds or 35% of the average patch time. SS patches used a mean of 558 words, and lasted 234 sec (95CI 215,252). Non-SS patches used a mean of 654 words and lasted 286 sec (95CI 240,332). Conclusion: The most common patch structure consisted of participant introduction, data presentation, clarification of data, making the clinical decision, exchange of administrative information, and a sign off. Deviation from this SS resulted in longer patches. When a non-SS patch structure was used, the patching paramedic was tied up 25% longer and unavailable to provide patient care.
2
Hueso, Miguel, Lluís de Haro, Jordi Calabia, Rafael Dal-Ré, Cristian Tebé, Karina Gibert, Josep M. Cruzado, and Alfredo Vellido. "Leveraging Data Science for a Personalized Haemodialysis." Kidney Diseases 6, no. 6 (2020): 385–94. http://dx.doi.org/10.1159/000507291.
Full textAbstract:
<b><i>Background:</i></b> The 2019 Science for Dialysis Meeting at Bellvitge University Hospital was devoted to the challenges and opportunities posed by the use of data science to facilitate precision and personalized medicine in nephrology, and to describe new approaches and technologies. The meeting included separate sections for issues in data collection and data analysis. As part of data collection, we presented the institutional ARGOS e-health project, which provides a common model for the standardization of clinical practice. We also pay specific attention to the way in which randomized controlled trials offer data that may be critical to decision-making in the real world. The opportunities of open source software (OSS) for data science in clinical practice were also discussed. <b><i>Summary:</i></b> Precision medicine aims to provide the right treatment for the right patients at the right time and is deeply connected to data science. Dialysis patients are highly dependent on technology to live, and their treatment generates a huge volume of data that has to be analysed. Data science has emerged as a tool to provide an integrated approach to data collection, storage, cleaning, processing, analysis, and interpretation from potentially large volumes of information. This is meant to be a perspective article about data science based on the experience of the experts invited to the Science for Dialysis Meeting and provides an up-to-date perspective of the potential of data science in kidney disease and dialysis. <b><i>Key messages:</i></b> Healthcare is quickly becoming data-dependent, and data science is a discipline that holds the promise of contributing to the development of personalized medicine, although nephrology still lags behind in this process. The key idea is to ensure that data will guide medical decisions based on individual patient characteristics rather than on averages over a whole population usually based on randomized controlled trials that excluded kidney disease patients. Furthermore, there is increasing interest in obtaining data about the effectiveness of available treatments in current patient care based on pragmatic clinical trials. The use of data science in this context is becoming increasingly feasible in part thanks to the swift developments in OSS.
3
Shah, Adnan Muhammad, Wazir Muhammad, and KangYoon Lee. "Examining the Determinants of Patient Perception of Physician Review Helpfulness across Different Disease Severities: A Machine Learning Approach." Computational Intelligence and Neuroscience 2022 (February 26, 2022): 1–15. http://dx.doi.org/10.1155/2022/8623586.
Full textAbstract:
(1) Background. Patients are increasingly using physician online reviews (PORs) to learn about the quality of care. Patients benefit from the use of PORs and physicians need to be aware of how this evaluation affects their treatment decisions. The current work aims to investigate the influence of critical quantitative and qualitative factors on physician review helpfulness (RH). (2) Methods. The data including 45,300 PORs across multiple disease types were scraped from Healthgrades.com. Grounded on the signaling theory, machine learning-based mixed methods approaches (i.e., text mining and econometric analyses) were performed to test study hypotheses and address the research questions. Machine learning algorithms were used to classify the data set with review- and service-related features through a confusion matrix. (3) Results. Regarding review-related signals, RH is primarily influenced by review readability, wordiness, and specific emotions (positive and negative). With regard to service-related signals, the results imply that service quality and popularity are critical to RH. Moreover, review wordiness, service quality, and popularity are better predictors for perceived RH for serious diseases than they are for mild diseases. (4) Conclusions. The findings of the empirical investigation suggest that platform designers should design a recommendation system that reduces search time and cognitive processing costs in order to assist patients in making their treatment decisions. This study also discloses the point that reviews and service-related signals influence physician RH. Using the machine learning-based sentic computing framework, the findings advance our understanding of the important role of discrete emotions in determining perceived RH. Moreover, the research also contributes by comparing the effects of different signals on perceived RH across different disease types.
4
Bylone, Mary. "Effective Decision Making: Data, Data, and More Data!" AACN Advanced Critical Care 21, no. 2 (April 1, 2010): 130–32. http://dx.doi.org/10.4037/15597768-2010-2003.
Full text
5
Goldman, Gilbert M., and Thyyar M. Ravindranath. "The Contextual Nature of Critical Care Judgment." Journal of Intensive Care Medicine 9, no. 2 (March 1994): 58–63. http://dx.doi.org/10.1177/088506669400900202.
Full textAbstract:
Critical care decision-making involves principles common to all medical decision-making. However, critical care is a remarkably distinctive form of clinical practice and therefore it may be useful to distinguish those elements particularly important or unique to ICU decision-making. The peculiar contextuality of critical care decision-making may be the best example of these elements. If so, attempts to improve our understanding of ICU decision-making may benefit from a formal analysis of its remarkable contextual nature. Four key elements of the context of critical care decisions can be identified: (1) costs, (2) time constraints, (3) the uncertain status of much clinical data, and (4) the continually changing environment of the ICU setting. These 4 elements comprise the context for the practice of clinical judgment in the ICU. The fact that intensivists are severely constrained by teh context of each case has important ramifications both for practice and for retrospective review. During retrospective review, the contextual nature of ICU judgment may be unfairly neglected by ignoring one or more of the key elements. Such neglect can be avoided if intensivists demand empathetic evaluation from reviewers.
6
Saravanan, Pratima, and Jessica Menold. "Developing an Evidence-Based Clinical Decision-Support System to Enhance Prosthetic Prescription." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 10, no. 1 (June 2021): 121–25. http://dx.doi.org/10.1177/2327857921101116.
Full textAbstract:
With the rapid increase in the global amputee population, there is a clear need to assist amputee care providers with their decision-making during the prosthetic prescription process. To achieve this, an evidence-based decision support system that encompasses existing literature, current decision-making strategies employed by amputee care providers and patient-specific factors is proposed. Based on an extensive literature review combined with natural language processing and expert survey, the factors influencing the current decision-making of amputee care providers in prosthetic prescription were identified. Following that, the decision-making strategies employed by expert and novice prosthetists were captured and analyzed. Finally, a fundamental understanding of the effect gait analysis has on the decision-making strategies of prosthetists was studied. Findings from this work lay the foundation for developing a real-time decision support system integrated with a portable gait analysis tool to enhance prescription processes. This is critical in the low-income countries where there is a scarcity of amputee care providers and resources for an appropriate prescription.
7
Larionova, Regina. "INTELLIGENT DECISION-MAKING SUPPORT ALGORITHMS FOR HEALTH-CARE INSTITUTIONS." Applied Mathematics and Control Sciences, no. 1 (April 14, 2021): 81–94. http://dx.doi.org/10.15593/2499-9873/2021.1.05.
Full textAbstract:
The aim of the article is to develop algorithms of intellectual support for managerial decision-making in a preventive medical institution by medical staff on loading/reserving the number of patients to be served. The article substantiates the relevance of improving the mechanisms of management of non-stationary processes of medical services by health care institution (HCP) on the basis of subject-oriented modeling of its activities as a system of mass service. The article offers tools for an integrated assessment of the current state of LPI as a socio-economic system for the substantiation of necessary measures to ensure the required level of readiness of LPI. The article touches upon the problem of determining the functional completeness of LFU, and in this connection, the range of seasonal planning is considered. The paper highlights the issues of the coordination procedure in the formation of a comprehensive assessment of LPF on the reserve/loading issues. The author proposes a mechanism for processing the data coming from LPFs according to the predicate, on the basis of which an automated data processing procedure can be built as an addition in the formation of a comprehensive assessment of LPF load/reserve. These algorithms are scientifically new and make it possible to monitor the current state of the mass service system and predict the functional completeness/incompleteness of the system with justification of necessary correction of its parameters. In the article the analysis of occurrence of typical problems of identification of typical situations and recommended actions for bringing LRC to a new state, in the best way, corresponding to the task of the guaranteed granting of medical services to the population before the moment of the new primary information is resulted. The author has proposed the use of simulation results in the formation of a functional management of the state of health care facilities. The proposed intelligent control mechanisms ergonomically well match the capabilities of the personnel of usual qualification.
8
Cole, William G., and James G. Stewart. "Metaphor graphics to support integrated decision making with respiratory data." International Journal of Clinical Monitoring and Computing 10, no. 2 (May 1993): 91–100. http://dx.doi.org/10.1007/bf01142279.
Full text
9
Berg, Sheri M., and Edward A. Bittner. "Disrupting Deficiencies in Data Delivery and Decision-Making During Daily ICU Rounds*." Critical Care Medicine 47, no. 3 (March 2019): 478–79. http://dx.doi.org/10.1097/ccm.0000000000003605.
Full text
10
Parra-Rodriguez, Luis, and M. Cristina Vazquez Guillamet. "Antibiotic Decision-Making in the ICU." Seminars in Respiratory and Critical Care Medicine 43, no. 01 (February 2022): 141–49. http://dx.doi.org/10.1055/s-0041-1741014.
Full textAbstract:
AbstractIt is well established that Intensive Care Units (ICUs) are a focal point in antimicrobial consumption with a major influence on the ecological consequences of antibiotic use. With the high prevalence and mortality of infections in critically ill patients, and the clinical challenges of treating patients with septic shock, the impact of real life clinical decisions made by intensivists becomes more significant. Both under- and over-treatment with unnecessarily broad spectrum antibiotics can lead to detrimental outcomes. Even though substantial progress has been made in developing rapid diagnostic tests that can help guide antibiotic use, there is still a time window when clinicians must decide the empiric antibiotic treatment with insufficient clinical data. The continuous streams of data available in the ICU environment make antimicrobial optimization an ongoing challenge for clinicians but at the same time can serve as the input for sophisticated models. In this review, we summarize the evidence to help guide antibiotic decision-making in the ICU. We focus on 1) deciding if to start antibiotics, 2) choosing the spectrum of the empiric agents to use, and 3) de-escalating the chosen empiric antibiotics. We provide a perspective on the role of machine learning and artificial intelligence models for clinical decision support systems that can be incorporated seamlessly into clinical practice in order to improve the antibiotic selection process and, more importantly, current and future patients' outcomes.
11
Quinn, Jill R., Madeline Schmitt, Judith Gedney Baggs, Sally A. Norton, Mary T. Dombeck, and Craig R. Sellers. "Family Members’ Informal Roles in End-of-Life Decision Making in Adult Intensive Care Units." American Journal of Critical Care 21, no. 1 (January 1, 2012): 43–51. http://dx.doi.org/10.4037/ajcc2012520.
Full textAbstract:
Background To support the process of effective family decision making, it is important to recognize and understand informal roles that various family members may play in the end-of-life decision-making process. Objective To describe some informal roles consistently enacted by family members involved in the process of end-of-life decision making in intensive care units. Methods Ethnographic study. Data were collected via participant observation with field notes and semistructured interviews on 4 intensive care units in an academic health center in the mid-Atlantic United States from 2001 to 2004. The units studied were a medical, a surgical, a burn and trauma, and a cardiovascular intensive care unit. Participants Health care clinicians, patients, and family members. Results Informal roles for family members consistently observed were primary caregiver, primary decision maker, family spokesperson, out-of-towner, patient’s wishes expert, protector, vulnerable member, and health care expert. The identified informal roles were part of families’ decision-making processes, and each role was part of a potentially complicated family dynamic for end-of-life decision making within the family system and between the family and health care domains. Conclusions These informal roles reflect the diverse responses to demands for family decision making in what is usually a novel and stressful situation. Identification and description of these informal roles of family members can help clinicians recognize and understand the functions of these roles in families’ decision making at the end of life and guide development of strategies to support and facilitate increased effectiveness of family discussions and decision-making processes.
12
Phillips, Georgina, Kate Lifford, Adrian Edwards, Marlise Poolman, and Natalie Joseph-Williams. "Do published patient decision aids for end-of-life care address patients’ decision-making needs? A systematic review and critical appraisal." Palliative Medicine 33, no. 8 (June 14, 2019): 985–1002. http://dx.doi.org/10.1177/0269216319854186.
Full textAbstract:
Background: Many decisions are made by patients in their last months of life, creating complex decision-making needs for these individuals. Identifying whether currently existing patient decision aids address the full range of these patient decision-making needs will better inform end-of-life decision support in clinical practice. Aims and design: This systematic review aimed to (a) identify the range of patients’ decision-making needs and (b) assess the extent to which patient decision aids address these needs. Data sources: MEDLINE, PsycINFO and CINAHL electronic literature databases were searched (January 1990–January 2017), supplemented by hand-searching strategies. Eligible literature reported patient decision-making needs throughout end-of-life decision-making or were evaluations of patient decision aids. Identified decision aid content was mapped onto and assessed against all patient decision-making needs that were deemed ‘addressable’. Results: Twenty-two studies described patient needs, and seven end-of-life patient decision aids were identified. Patient needs were categorised, resulting in 48 ‘addressable’ needs. Mapping needs to patient decision aid content showed that 17 patient needs were insufficiently addressed by current patient decision aids. The most substantial gaps included inconsistent acknowledgement, elicitation and documentation of how patient needs varied individually for the level of information provided, the extent patients wanted to participate in decision-making, and the extent they wanted their families and associated healthcare professionals to participate. Conclusion: Patient decision-making needs are broad and varied. Currently developed patient decision aids are insufficiently addressing patient decision-making needs. Improving future end-of-life patient decision aid content through five key suggestions could improve patient-focused decision-making support at the end of life.
13
Tan, Joseph, and Fuchung Wang. "Non-Traditional Data Mining Applications in Taiwan National Health Insurance (NHI) Databases." International Journal of Healthcare Information Systems and Informatics 12, no. 4 (October 2017): 31–51. http://dx.doi.org/10.4018/ijhisi.2017100103.
Full textAbstract:
This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance (NHI) databases. In order to obtain the best payment management, a hybrid mining (HM) approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytic processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will assist in directing the health insurance decision-making process, is built. Drawing from lessons learned within a case study setting, results showed that not only is HM approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Essentially, HM approach can provide a critical boost to health insurance decision support; hence, future researchers should develop and improve the approach combined with their own application systems.
14
Pickering, Brian W., Ognjen Gajic, Adil Ahmed, Vitaly Herasevich, and Mark T. Keegan. "Data Utilization for Medical Decision Making at the Time of Patient Admission to ICU*." Critical Care Medicine 41, no. 6 (June 2013): 1502–10. http://dx.doi.org/10.1097/ccm.0b013e318287f0c0.
Full text
15
Pattison, Natalie, Geraldine O’Gara, and Timothy Wigmore. "Negotiating Transitions: Involvement of Critical Care Outreach Teams in End-of-Life Decision Making." American Journal of Critical Care 24, no. 3 (May 1, 2015): 232–40. http://dx.doi.org/10.4037/ajcc2015715.
Full textAbstract:
Background Little research has examined the involvement of critical care outreach teams in end-of-life decision making. Objective To establish how much time critical care outreach teams spend with patients who are subsequently subject to limitation of medical treatment and end-of-life decisions and how much influence the teams have on those decisions. Methods A single-center retrospective review, with qualitative analysis, in a large cancer center. Data from all patients referred emergently for critical care outreach from October 2010 to October 2011 who later had limitation of medical treatment or end-of-life care were retrieved. Findings were analyzed by using SPSS 19 and qualitative free-text analysis. Results Of 890 patients referred for critical care outreach from October 2010 to October 2011, 377 were referred as an emergency; 108 of those had limitation of medical treatment and were included in the review. Thirty-five patients (32.4%) died while hospitalized. As a result of outreach intervention and a decision to limit medical treatment, 56 (51.9%) of the 108 patients received a formal end-of-life care plan (including care pathways, referral to palliative care team, hospice). About a fifth (21.5%) of clinical contact time is being spent on patients who subsequently are subject to limitation of medical treatment. Qualitative document analysis showed 5 emerging themes: difficulty of discussions about not attempting cardiopulmonary resuscitation, complexities in coordinating multiple teams, delays in referral and decision making, decision reversals and opaque decision making, and technical versus ethical imperatives. Conclusion A considerable amount of time is being spent on these emergency referrals, and decisions to limit medical treatment are common. The appropriateness of escalation of levels of care is often not questioned until patients become critically or acutely unwell, and outreach teams subsequently intervene.
16
Kissné Horváth, Ildikó. "Patient registries from the view of health policy." Orvosi Hetilap 155, no. 19 (May 2014): 729–31. http://dx.doi.org/10.1556/oh.2014.29917.
Full textAbstract:
Integrated health data management and disease registries which are able to support evidence-based decision making are of critical importance for health policy. Data provided by disease registries are used for the development of health strategy, planning of preventive activities, capacity-building in health care provision, improving health care quality, and planning clinical trials. Disease registries monitoring epidemiology, natural history of diseases, treatment outcomes and the detection of adverse reactions are requested not only by policy-makers, but public health authorities and health care providers, too. Registries for rare diseases are of critical importance for developing network between reference centres and developing and evaluating new drugs. Data and information need for decision-making in public services and the protection of health data of individuals require a careful balance that needs to be taken into account when considering disease registries. Orv. Hetil., 2014, 155(19), 729–731.
17
Huffines, Meredith, Karen L. Johnson, Linda L. Smitz Naranjo, Matthew E. Lissauer, Marmie Ann-Michelle Fishel, Susan M. D’Angelo Howes, Diane Pannullo, Mindy Ralls, and Ruth Smith. "Improving Family Satisfaction and Participation in Decision Making in an Intensive Care Unit." Critical Care Nurse 33, no. 5 (October 1, 2013): 56–69. http://dx.doi.org/10.4037/ccn2013354.
Full textAbstract:
Background Survey data revealed that families of patients in a surgical intensive care unit were not satisfied with their participation in decision making or with how well the multidisciplinary team worked together. Objectives To develop and implement an evidence-based communication algorithm and evaluate its effect in improving satisfaction among patients’ families. Methods A multidisciplinary team developed an algorithm that included bundles of communication interventions at 24, 72, and 96 hours after admission to the unit. The algorithm included clinical triggers, which if present escalated the algorithm. A pre-post design using process improvement methods was used to compare families’ satisfaction scores before and after implementation of the algorithm. Results Satisfaction scores for participation in decision making (45% vs 68%; z = −2.62, P = .009) and how well the health care team worked together (64% vs 83%; z = −2.10, P = .04) improved significantly after implementation. Conclusions Use of an evidence-based structured communication algorithm may be a way to improve satisfaction of families of intensive care patients with their participation in decision making and their perception of how well the unit’s team works together.
18
Özcan, Fatma. "Reflections on my data management research journey (VLDB women in database research award talk)." Proceedings of the VLDB Endowment 15, no. 12 (August 2022): 3821–22. http://dx.doi.org/10.14778/3554821.3554903.
Full textAbstract:
Data-driven decision making is critical for all kinds of enterprises, public and private. It has been my mission to find more efficient, and effective ways to store, manage, query and analyze data to drive actionable insights. Throughout my career, I worked on many different technologies and systems, including semi-structured query processing, and large-scale data analytics. In this talk, I will talk about lessons learned, both technical and non-technical, using two of these systems as examples.
19
Lovell, Ben. "Editorial Volume 18 Issue 4 – Decision-making in acute medicine." Acute Medicine Journal 18, no. 4 (October 1, 2019): 206–7. http://dx.doi.org/10.52964/amja.0777.
Full textAbstract:
Acute physicians make patient-centred decisions at the start of the patient’s hospital journey. Dozens more decisions are made by the individual members of the MDT (and, of course, by the patient) during the in-patient period. Decisions are made at every level of seniority and experience and range widely in scope and impact. The original articles in this issue are connected by a common thread: phenomena that inform and influence the decisions made by acute physicians. How do you obtain adequate data to make sound decisions about individual patient care? It is often necessary to collate data around previous admissions and investigations at other healthcare institutions; a process fraught with complications. In this issue Ghelani et al1 undertook the onerous task of calling the main switchboard of all 175 acute hospitals in England on six occasions. Their aim was to identify how long it takes for an outside caller to finally contact a human operative who could put them through to the the correct person or extension. Most healthcare professionals will have some insight into the communication barriers that lie between practitioners in different hospitals, and the inordinate amount of time spent on the phone trying to gain patient information from another institution. The authors’ findings that automated messages and call steering systems impact significantly upon the time required for straightforward datagathering tasks should resonate with many for us. Hopefully, this study will provide substrate for future quality improvement efforts in the UK. How do you decide if your patient is well enough for transfer? The National Early Warning Score (NEWS) is now common currency in acute hospitals. This simple aggregate scoring system uses physiological parameters measures at the bedside and is used to inform assessments about patient acuity. There is currently significant research attention focused on the NEWS’ powers of prognostication. In a previous issues of this journal, we reported how vital sign abnormalities in the Emergency Department are predictors of poor outcomes (although not mortality) 2; monitoring of post-discharge vital signs in the community may predict readmission;3 and minor fluctuations in respiratory rate (in combination with other vital signs) may predicted clinical outcomes several days in advance.4 In this issue Subbe et all5 explore whether patients with low or unchanging NEWS scores are unlikely to deteriorate in future and could therefore be considered for transfer. How do you decide whether your patient requires intravenous fluids? In their qualitative study, Lloyd et al 6thematically analysed data from interviews with clinicians to better delineate the decision-making processes surrounding fluid therapy. They describe how doctors use vital signs, clinical presentation and their own gestalt, and – curiously – these approaches may be affected by the clinical environment and workload, and are not informed by local or national guidelines.
20
Gotur, Deepa Bangalore, Faisal Masud, Jaya Paranilam, and Janice L. Zimmerman. "Analysis of Rothman Index Data to Predict Postdischarge Adverse Events in a Medical Intensive Care Unit." Journal of Intensive Care Medicine 35, no. 6 (May 2, 2018): 606–10. http://dx.doi.org/10.1177/0885066618770128.
Full textAbstract:
Objective: Currently, there are no objective metrics included in the intensive care unit (ICU) discharge decision making process. In this study, we evaluate Rothman Index(RI) data for a possible metric as part of a quality improvement project. Our objectives were to determine whether RI could predict adverse events occurring within 72 hours of ICU discharge decision, the optimal clinical cutoff value for this metric, and to determine whether there is a relation between the RI warning alert 24 hours prior to discharge and adverse events postdischarge. Design: Retrospective observational study. Setting: Single center tertiary hospital. Patients: Adult medical ICU patients discharged from the ICU between January 20, 2015 and March 14, 2015. Interventions: None. Measurements and Main Results: A total of 194 patients were studied with mean age of 62.74 (18.37) years. Data collection included RI at the time of decision-making for ICU discharge and the presence of any warning signals in the previous 24 hours. A 72-hour follow-up chart review recorded any adverse events, including readmission to a higher level of care, discontinuation of discharge due to clinical status change, emergency department visit if discharged home, rapid response activation, or cardiopulmonary arrest postdischarge. Adverse events after ICU discharge were observed in 31 (16%) patients with 9 events being ICU readmission (4.6%). Based on an age-adjusted multivariate model, a higher RI was associated with lower odds of an adverse event (odds ratio [OR] = 0.969, P = .006, confidence interval [CI]: 0.9487-0.9911). An RI value ≥ 50 was associated with 72% lower odds of an adverse event (OR = 0.2887, 95% CI = 0.1278-0.6517 and P = .003) compared to RI < 50. This RI cutoff value was associated with the largest decrease in odds of events. As expected, patients with a very high-risk warning alert had a higher proportion of adverse events compared to patients who did not. (31.75% vs 12.65%, P = < .02). Conclusions: Patients who have an RI < 50 or a very high-risk warning alert have a higher risk of adverse events postdischarge from the ICU. Rothman Index may be a useful metric for ICU discharge decision-making.
21
Voss, Sarah, Kim Kirby, Janet Brandling, and Jonathan Benger. "OP6 A qualitative study on conveyance decision-making during emergency call outs to people with dementia: The HOMEWARD project." Emergency Medicine Journal 36, no. 10 (September 24, 2019): e4.2-e4. http://dx.doi.org/10.1136/emermed-2019-999abs.6.
Full textAbstract:
ObjectivesAmbulance staff are increasingly required to make complex decisions as to whether they should convey a patient to hospital or ‘see and treat’ at the scene. Dementia can be a significant barrier to the assessment of pain and injury. However, to our knowledge no research has specifically examined the process of decision-making by ambulance staff in relation to people with dementia. This qualitative study was designed to investigate the factors influencing the decision-making process during ambulance calls to older people with dementia.MethodsThis qualitative study used a combination of observation, interview and document analysis, to investigate the factors influencing the decision-making process during ambulance calls to older people with dementia. A researcher worked alongside ambulance crews in the capacity of observer and recruited eligible patients to participate in case studies. Data were collected from observation notes of decision-making during the incident, patient care records and post incident interviews with participants, and analysed thematically.ResultsFour main themes emerged from the data concerning the way that paramedics make decisions in people with dementia: Physical Condition; the key factor influencing paramedics’ decision-making was the physical condition of the patient. Cognitive Capacity; most of the participants preferred not to remove patients with a diagnosis of dementia from surroundings familiar to them, unless they deemed it absolutely essential. Patient Circumstances; this included the patient’s medical history and the support available to them. Professional Influences; paramedics also drew on other perspectives to inform their decision-making.ConclusionThe preference for avoiding unnecessary conveyance for patients with dementia, combined with difficulties in obtaining an accurate patient medical history and assessment, mean that decision-making can be especially challenging. Further research is needed to find reliable ways of assessing patients and accessing information to support conveyance decisions for ambulance calls to people with dementia.
22
Omundsen, Heidi C., Renee L. Franklin, Vicki L. Higson, Mark S. Omundsen, and Jeremy I. Rossaak. "Perioperative shared decision-making in the Bay of Plenty, New Zealand: Audit results from a complex decision pathway quality improvement initiative using a structured communication tool." Anaesthesia and Intensive Care 48, no. 6 (November 2020): 473–76. http://dx.doi.org/10.1177/0310057x20960734.
Full textAbstract:
Patients presenting for elective surgery in the Bay of Plenty area in New Zealand are increasingly elderly with significant medical comorbidities. For these patients the risk–benefit balance of undergoing surgery can be complex. We recognised the need for a robust shared decision-making pathway within our perioperative medicine service. We describe the setup of a complex decision pathway within our district health board and report on the audit data from our first 49 patients. The complex decision pathway encourages surgeons to identify high-risk patients who will benefit from shared decision-making, manages input from multiple specialists as needed with excellent communication between those specialists, and provides a patient-centred approach to decision-making using a structured communication tool.
23
Ladin, Keren, Renuka Pandya, Ronald D. Perrone, Klemens B. Meyer, Allison Kannam, Rohini Loke, Tira Oskoui, Daniel E. Weiner, and John B. Wong. "Characterizing Approaches to Dialysis Decision Making with Older Adults." Clinical Journal of the American Society of Nephrology 13, no. 8 (July 26, 2018): 1188–96. http://dx.doi.org/10.2215/cjn.01740218.
Full textAbstract:
Background and objectivesDespite guidelines recommending shared decision making, nephrologists vary significantly in their approaches to discussing conservative management for kidney replacement therapy with older patients. Many older patients do not perceive dialysis initiation as a choice or receive sufficient information about conservative management for reasons incompletely understood. We examined how nephrologists’ perceptions of key outcomes and successful versus failed treatment discussions shape their approach and characterized different models of decision making, patient engagement, and conservative management discussion.Design, setting, participants, & measurementsOur qualitative study used semistructured interviews with a sample of purposively sampled nephrologists. Interviews were conducted from June 2016 to May 2017 and continued until thematic saturation. Data were analyzed using typological and thematic analyses.ResultsAmong 35 nephrologists from 18 practices, 20% were women, 66% had at least 10 years of nephrology experience, and 80% were from academic medical centers. Four distinct approaches to decision making emerged: paternalist, informative (patient led), interpretive (navigator), and institutionalist. Five themes characterized differences between these approaches, including patient autonomy, engagement and deliberation (disclosing all options, presenting options neutrally, eliciting patient values, and offering explicit treatment recommendation), influence of institutional norms, importance of clinical outcomes (e.g., survival and dialysis initiation), and physician role (educating patients, making decisions, pursuing active therapies, and managing symptoms). Paternalists and institutionalists viewed initiation of dialysis as a measure of success, whereas interpretive and informative nephrologists focused on patient engagement, quality of life, and aligning patient values with treatment. In this sample, only one third of providers presented conservative management to patients, all of whom followed either informative or interpretive approaches. The interpretive model best achieved shared decision making.ConclusionsDifferences in nephrologists’ perceptions of their role, patient autonomy, and successful versus unsuccessful encounters contribute to variation in decision making for patients with kidney disease.
24
Konchak, Chad W., Jacob Krive, Loretta Au, Daniel Chertok, Priya Dugad, Gus Granchalek, Ekaterina Livschiz, et al. "From Testing to Decision-Making: A Data-Driven Analytics COVID-19 Response." Academic Pathology 8 (January 1, 2021): 237428952110102. http://dx.doi.org/10.1177/23742895211010257.
Full textAbstract:
In March 2020, NorthShore University Health System laboratories mobilized to develop and validate polymerase chain reaction based testing for detection of SARS-CoV-2. Using laboratory data, NorthShore University Health System created the Data Coronavirus Analytics Research Team to track activities affected by SARS-CoV-2 across the organization. Operational leaders used data insights and predictions from Data Coronavirus Analytics Research Team to redeploy critical care resources across the hospital system, and real-time data were used daily to make adjustments to staffing and supply decisions. Geographical data were used to triage patients to other hospitals in our system when COVID-19 detected pavilions were at capacity. Additionally, one of the consequences of COVID-19 was the inability for patients to receive elective care leading to extended periods of pain and uncertainty about a disease or treatment. After shutting down elective surgeries beginning in March of 2020, NorthShore University Health System set a recovery goal to achieve 80% of our historical volumes by October 1, 2020. Using the Data Coronavirus Analytics Research Team, our operational and clinical teams were able to achieve 89% of our historical volumes a month ahead of schedule, allowing rapid recovery of surgical volume and financial stability. The Data Coronavirus Analytics Research Team also was used to demonstrate that the accelerated recovery period had no negative impact with regard to iatrogenic COVID-19 infection and did not result in increased deep vein thrombosis, pulmonary embolisms, or cerebrovascular accident. These achievements demonstrate how a coordinated and transparent data-driven effort that was built upon a robust laboratory testing capability was essential to the operational response and recovery from the COVID-19 crisis.
25
Zollo, MB, JC Moskop, and Kahn CEJr. "Knowing the score: using predictive scoring systems in clinical practice." American Journal of Critical Care 5, no. 2 (March 1, 1996): 147–51. http://dx.doi.org/10.4037/ajcc1996.5.2.147.
Full textAbstract:
Outcome scores have been promoted as adjuncts to clinical decision making, especially when further care is thought to be futile. The Pediatric Risk of Mortality score is used to calculate the risk of mortality for patients admitted to pediatric intensive care units. In this article the Pediatric Risk of Mortality score in evaluated for its ability to contribute to individual patient care decisions in the context of clinical practice. Through analysis several features of the Pediatric Risk of Mortality score were identified that require discretion if the score is to be used in decisions involving individual patients. These features include variability and bias introduced in data collection and data presentation. Also, outcome scores do not allow for the incorporation of patient and family values into the decision process. Outcome scores can provide some adjunctive information to clinicians, but they should be used with caution when making patient care decisions. Use of Pediatric Risk of Mortality scores in clinical practice must be tempered with a knowledge of the limitations of the scores, individual patient variability, the conditions under which the scores have been validated and collected and, most importantly, an awareness that outcome scores do not take into account the caregiver and patient values that are inherent in any treatment decision.
26
Jacob, DA. "Family members' experiences with decision making for incompetent patients in the ICU: a qualitative study." American Journal of Critical Care 7, no. 1 (January 1, 1998): 30–36. http://dx.doi.org/10.4037/ajcc1998.7.1.30.
Full textAbstract:
BACKGROUND: Understanding the challenges faced by family members involved in decisions about the use of life-sustaining treatment for incompetent patients in the ICU is necessary for developing empirically based supportive interventions. OBJECTIVES: To describe and explain the experiences of family members who were involved in decisions on behalf of their loved ones in order to promote understanding of such experiences and to suggest areas for effective, supportive intervention. METHODS: The grounded-theory method of qualitative research was used. Data collection involved semistructured interviews of 17 persons who had been involved in decisions about the use of life-sustaining treatment for a family member in the ICU. RESULTS: Family members discussed the need to arrive at a judgment of the patient's condition and to work with caregivers to have the family member's decision about life-sustaining treatment enacted. Data analysis suggests that clinicians can best support family members by helping the members arrive at a judgment about the patient's condition and treatment desires and by connecting with the family members to ensure that treatment goals are mutual. Supporting family members in this way helps them accept and go on in a positive way after the experience. CONCLUSIONS: Family members of patients in the ICU are willing and able to take responsibility for decisions about the use of life-sustaining treatment for their loved ones. The long-term acceptance of the experience and the decisions made depends greatly on the interactions between the family member who makes the decision and nurses and physicians in the clinical setting.
27
Thompson, Carla J., Nancy Bridier, Lesley Leonard, and Steve Morse. "Exploring stress, coping, and decision-making considerations of Alzheimer’s family caregivers." Dementia 19, no. 6 (November 28, 2018): 1907–26. http://dx.doi.org/10.1177/1471301218809865.
Full textAbstract:
More than 15 million Americans are providing care for a family member with Alzheimer’s disease. Family caregivers are faced with highly stressful experiences, using strong coping skills, and implementing critical decisions with little or no knowledge or information and with virtually no preparation or assistance. The need for research efforts to focus on caregiver stress, coping mechanisms, and informed decision-making skills spearheaded a theoretical framework to study the potential relationships between family caregivers’ types of stress, coping skills, and their decision-making efforts. Constructs of life event stress, role strain, self-concept stress, and coping stress were examined relative to 10 priority areas of decision-making identified by the national Alzheimer’s Association. A relational non-experimental research design was utilized. Caregivers completed four Likert-scale instruments with data analyzed using descriptive statistics and rank-order correlation procedures. Findings indicated varying levels of stress, strong family self-efficacy and high levels of coping skills contribute to critical decision-making.
28
Cui, Nianqi, Ruolin Qiu, Yuping Zhang, Dandan Chen, Hui Zhang, Hongyu Rao, and Jingfen Jin. "Why are physical restraints still in use? A qualitative descriptive study from Chinese critical care clinicians’ perspectives." BMJ Open 11, no. 11 (November 2021): e055073. http://dx.doi.org/10.1136/bmjopen-2021-055073.
Full textAbstract:
ObjectivesTo understand why critical care clinicians still implement physical restraints, to prevent unplanned extubation and to explore the driving factors influencing the decision-making of physical restraints use.DesignA qualitative descriptive design was used. The data were collected through one-to-one, semistructured interviews and analysed through the framework of thematic analysis.Participants and settingThe study was conducted from December 2019 to May 2020 at one general intensive care unit (ICU) and one emergency ICU in a general tertiary hospital with 3200 beds in Hangzhou, China. The sampling strategy was combined maximum variation sampling and criterion sampling.ResultsA total of 14 clinicians participated in the study. The reason why critical care clinicians implemented physical restraints to prevent unplanned extubation was that the tense healthcare climate was caused by family members’ rejection of mismatched expectations. As unplanned extubation was highly likely to create medical disputes, hospitals placed excessive emphasis on unplanned extubation, which resulted in a lack of analysis of the cause of unplanned extubation and strict measures for dealing with unplanned extubation. The shortage of nursing human resources, unsuitable ward environments, intensivists’ attitudes, timely extubation for intensivists, nurse experiences and the patient’s possibility of unplanned extubation all contributed to the decision-making resulting in the use of physical restraints.ConclusionsAlthough nurses played a crucial role in the decision-making process of using physical restraints, changing the healthcare climate and the hospital management mode for unplanned extubation are fundamental measures to reduce physical restraints use.
29
Hirshberg, Eliotte L., Jorie Butler, Morgan Francis, Francis A. Davis, Doriena Lee, Fahina Tavake-Pasi, Edwin Napia, et al. "Persistence of patient and family experiences of critical illness." BMJ Open 10, no. 4 (April 2020): e035213. http://dx.doi.org/10.1136/bmjopen-2019-035213.
Full textAbstract:
ObjectiveTo investigate: (1) patient and family experiences with healthcare and the intensive care unit (ICU); (2) experiences during their critical illness; (3) communication and decision making during critical illness; (4) feelings about the ICU experience; (5) impact of the critical illness on their lives; and (6) concerns about their future after the ICU.DesignFour semistructured focus group interviews with former ICU patients and family members.SettingsMulticultural community group and local hospitals containing medical/surgical ICUs.ParticipantsPatients and family who experienced a critical illness within the previous 10 years.InterventionsNone.Measurements and main resultsFour separate focus groups each lasting a maximum of 150 min and consisting of a total of 21 participants were held. Focus groups were conducted using a semistructured script including six topics relating to the experience of critical illness that facilitated deduction and the sorting of data by thematic analysis into five predominant themes. The five main themes that emerged from the data were: (1) personalised stories of the critical illness; (2) communication and shared decision making, (3) adjustment to life after critical illness, (4) trust towards clinical team and relevance of cultural beliefs and (5) end-of-life decision making. Across themes, we observed a misalignment between the medical system and patient and family values and priorities.ConclusionsThe experience of critical illness of a diverse group of patients and families can remain vivid for years after ICU discharge. The identified themes reflect the strength of memory of such pivotal experiences and the importance of a narrative around those experiences. Clinicians need to be aware of the lasting effects of critical illness has on patients and families.
30
Goran, Susan F., and Margaret Mullen-Fortino. "Partnership for a Healthy Work Environment." AACN Advanced Critical Care 23, no. 3 (July 1, 2012): 289–301. http://dx.doi.org/10.4037/nci.0b013e31825c1cc2.
Full textAbstract:
The tele–intensive care unit (ICU) provides a remote monitoring system that adds an additional layer of support for critically ill patients. However, to optimize contributions, the bedside team must incorporate this resource into the patient’s plan of care. Using the American Association of Critical-Care Nurses’ Healthy Work Environment Standards as a platform, we can create and nurture a new partnership model. Strategies that embrace the standards of skilled communication, true collaboration, and effective decision making become mutual goals for improving patient safety and outcomes. Joint communication guidelines facilitate timely and meaningful communication. Trust and the desire to cooperate encourage provider engagement to strengthen collaboration. The use of tele-ICU technology can assist in the interpretation and transformation of data to affect decision making at all levels to influence patient care. Through the lens of the healthy work environment, the tele-ICU/ICU partnership provides enhanced opportunities for improved patient care and team satisfaction.
31
Young, Jeffrey S., Robert L. Smith, Stephanie Guerlain, and Barbara Nolley. "How Residents Think and Make Medical Decisions: Implications for Education and Patient Safety." American Surgeon 73, no. 6 (June 2007): 548–53. http://dx.doi.org/10.1177/000313480707300604.
Full textAbstract:
Medical errors are a major cause of morbidity and mortality, and cognitive errors account for many of these events. This study examined the basic science of the cognitive performance of trainees. We created a low-intensity medical simulator to perform a preliminary study of the ability of residents to recall and process patient information presented verbally. The subjects were separated into three categories based on critical care experience: novice (≤8 weeks of critical care experience), intermediate (8–16 weeks of critical care experience), and expert (>16 weeks of critical care experience). The subjects were presented with three clinical cases. In the first case, the presentation contained 55 separate data points and subject recall was analyzed. In the second and third cases, a patient report was given, and the subjects were asked by a “medical student” to outline and explain their treatment decisions. Fifteen subjects completed the experiment (five novices, six intermediates, and four experts). Case 1 (recall): No significant differences among groups with regard to errors or total data points recalled (however, subjects who chose not to take notes had significantly poorer recall and committed more errors). Cases 2 and 3 (cognition and decision making): Intermediates and experts made significantly fewer errors. More importantly, the reasoning process (forward hypothesis based) of the more experienced residents differed from novices. This preliminary study demonstrates that the cognitive processes used by residents experienced in critical care are quantitatively and qualitatively different from those used by novices. These processes were also associated with far fewer cognitive errors in clinical decision making.
32
Cohen-Mansfield, Jiska, and Steven Lipson. "Medical Decision-Making around the Time of Death of Cognitively Impaired Nursing Home Residents: A Pilot Study." OMEGA - Journal of Death and Dying 48, no. 2 (March 2004): 103–14. http://dx.doi.org/10.2190/4j17-px0v-wq03-cgda.
Full textAbstract:
The purpose of this article is to describe the end-of-life process in the nursing home for three groups of cognitively–impaired nursing home residents: those who died with a medical decision-making process prior to death; those who died without such a decision-making process; and those who had a status–change event and a medical decision-making process, and did not die prior to data collection. Residents had experienced a medical status–change event within the 24 hours prior to data collection, and were unable to make their own decisions due to cognitive impairment. Data on the decision-making process during the event, including the type of event, the considerations used in making the decisions, and who was involved in making these decisions were collected from the residents' charts and through interviews with their physicians or nurse practitioners. When there was no decision-making process immediately prior to death, a decision-making process was usually reported to have occurred previously, with most decisions calling either for comfort care or limitation of care. When comparing those events leading to death with other status–change events, those who died were more likely to have suffered from troubled breathing than those who remained alive. Hospitalization was used only among those who survived, whereas diagnostic tests and comfort care were used more often with those who died. Those who died had more treatments considered and chosen than did those who remained alive. For half of those who died, physicians felt that they would have preferred less treatment for themselves if they were in the place of the decedents. The results represent preliminary data concerning decision-making processes surrounding death of the cognitively–impaired in the nursing home. Additional research is needed to elucidate the trends uncovered in this study.
33
Schumacher, Jessica R., David Zahrieh, Selina Chow, John Taylor, Rachel Wills, Bret M. Hanlon, Paul J. Rathouz, Jennifer L. Tucholka, and Heather B. Neuman. "Increasing socioeconomically disadvantaged patients’ engagement in breast cancer surgery decision-making through a shared decision-making intervention (A231701CD): protocol for a cluster randomised clinical trial." BMJ Open 12, no. 11 (November 2022): e063895. http://dx.doi.org/10.1136/bmjopen-2022-063895.
Full textAbstract:
IntroductionSocioeconomic disparities for breast cancer surgical care exist. Although the aetiology of the observed socioeconomic disparities is likely multifactorial, patient engagement during the surgical consult is critical. Shared decision-making may reduce health disparities by addressing barriers to patient engagement in decision-making that disproportionately impact socioeconomically disadvantaged patients. In this trial, we test the impact of a decision aid on increasing socioeconomically disadvantaged patients’ engagement in breast cancer surgery decision-making.Methods and analysisThis multisite randomised trial is conducted through 10 surgical clinics within the National Cancer Institute Community Oncology Research Program (NCORP). We plan a stepped-wedge design with clinics randomised to the time of transition from usual care to the decision aid arm. Study participants are female patients, aged ≥18 years, with newly diagnosed stage 0–III breast cancer who are planning breast surgery. Data collection includes a baseline surgeon survey, baseline patient survey, audio-recording of the surgeon–patient consultation, a follow-up patient survey and medical record data review. Interviews and focus groups are conducted with a subset of patients, surgeons and clinic stakeholders. The effectiveness of the decision aid at increasing patient engagement (primary outcome) is evaluated using generalised linear mixed-effects models. The extent to which the effect of the decision aid intervention on patient engagement is mediated through the mitigation of barriers is tested in joint linear structural equation models. Qualitative interviews explore how barriers impact engagement, especially for socioeconomically disadvantaged women.Ethics and disseminationThis protocol has been approved by the National Cancer Institute Central Institutional Review Board, and Certificate of Confidentiality has been obtained. We plan to disseminate the findings through journal publications and national meetings, including the NCORP network. Our findings will advance the science of medical decision-making with the potential to reduce socioeconomic health disparities.Trial registration numberClinicalTrials.gov Registry (NCT03766009).
34
Rababa, Mohammad, Dania Bani-Hamad, Audai A. Hayajneh, and Khalid Al Mugheed. "Nurses’ knowledge, attitudes, practice, and decision-making skills related to sepsis assessment and management." Electronic Journal of General Medicine 19, no. 6 (October 19, 2022): em420. http://dx.doi.org/10.29333/ejgm/12556.
Full textAbstract:
<b>Objectives</b>: The present study examines the critical care nurse’s knowledge, attitudes, practice (KAP), and decision-making related to early assessment and management of sepsis.<br /> <b>Methods</b>: This cross-sectional descriptive study utilized a convenience sample of 70 nurses working in a college hospital in the northern region of Jordan. Data were gathered employing a sepsis vignette and valid questionnaires via Google document. The nursing decision-making instrument and the knowledge, attitudes, and practice survey were utilized to assess nurses’ decision-making skills, knowledge, attitudes, and practice, respectively. Nurses’ sociodemographic/professional data, including gender, marital status, experience, education, and work environment, were also measured.<br /> <b>Result</b>: The participating nurses reported poor KAP, and analytical decision-making skills related to sepsis management. Experienced nurses and those with a master’s degree reported significantly better KAP, and intuitive decision-making skills than naïve and those with a bachelor’s degree. Nurses with analytical decision-making modes reported higher levels of knowledge, attitudes, and practice than nurses with intuitive or flexible analytical-intuitive decision-making modes.<br /> <b>Conclusion</b>: Poor decision-making skills, as well as knowledge, attitudes, and practice related to sepsis assessment and management, is a substantial problem that demands a productive re-evaluation of the current sepsis management practices. Boosting the knowledge and improving the practices on sepsis assessment and management through comprehensive educational programs and campaigns are necessary to improve nurses’ decision-making skills.
35
Bear, Alexandria, and Elizabeth Thiel. "Documentation of Crucial Information Relating to Critically Ill Patients." Journal of Palliative Care 33, no. 1 (December 20, 2017): 5–8. http://dx.doi.org/10.1177/0825859717746273.
Full textAbstract:
Background: Medical decision-making has evolved to the modern model of shared decision-making among patients, surrogate decision-makers, and medical providers. As such, informed consent discussions with critically ill patients often should include larger discussions relating to values and goals of care. Documentation of care options and prognosis serves as an important component of electronic communication relating to patient preferences among care providers. Objective: This retrospective chart review study sought to evaluate the prevalence of documentation of critical data, care options, prognosis, and medical plan, within primary team and palliative care consult team documentation. Results: Three hundred two electronic medical records were reviewed. There was a significant difference in documentation between palliative care and primary teams for prognosis (83% vs 32%, P < .001), care options (82% vs 50%, P < .001), and care plan (82% vs 46%, P < .001). Conclusions: Our retrospective chart review study demonstrated a significant difference in documentation between primary and palliative care teams. We acknowledge that review of documentation cannot be extrapolated to the presence or absence of conversations between providers and patients and/or surrogates. Additional studies to evaluate this connection would be advantageous.
36
Northam, Weston, Avinash Chandran, Crystal Adams, Nikki E. Barczak-Scarboro, and Carolyn Quinsey. "Cranioplasty length of stay: Relationship with indication, surgical decision-making factors, and sex." Trauma 22, no. 4 (December 6, 2019): 256–64. http://dx.doi.org/10.1177/1460408619892141.
Full textAbstract:
Objectives Cranioplasty is being performed more often due to rising rates of decompressive craniectomy. Hospital length of stay is a quality metric which has not been directly studied after cranioplasty. This study aims to identify factors associated with length of stay after cranioplasty to better understand their outcomes. Patients and methods A retrospective review was conducted at a single academic center from 2007 to 2015 for all patients >18 years of age who received cranioplasty. Baseline data from 148 patients were recorded including demographics, clinical characteristics, and surgeon decision-making factors for cranioplasty. Post-operative complications within 30 days after cranioplasty were recorded in addition to disposition and discharge data. Weibull accelerated failure time models were used to identify significant associations with length of stay after cranioplasty. Results The overall post-operative complication rate was 27.0%, and the most frequent indication for craniectomy was traumatic brain injury. The majority (72.3%) of patients returned home, compared to other disposition, and median length of stay was 2.0 days (interquartile range = 2.0). Average length of stay was 7.7 days in men, as compared with 2.4 days in women, and even upon adjusting for covariate effects, length of stay was longer in men than in women irrespective of post-operative complications. When time-to-cranioplasty fell between 0 and 30 days, average length of stay was 19.2 days, as compared with 10.3 days when time-to-cranioplasty fell between 30 and 90 days, and 2.5 days when time-to-cranioplasty was >90 days. After adjustment for covariate effects, the association between time-to-cranioplasty and length of stay was maintained only in patients without post-operative complications. Conclusions Length of stay can inform our understanding of outcomes after cranioplasty. In our study, length of stay was associated with sex, indication for craniectomy, and surgical decision-making (time-to-cranioplasty and implant material), but time-to-cranioplasty was only associated in patients without post-operative complications. These relationships should be seen not as direct causation, but rather as tools to add to our understanding of this relatively complicated procedure.
37
Patel, Siddharth M., Jacob C. Jentzer, Carlos L. Alviar, Vivian M. Baird-Zars, Gregory W. Barsness, David D. Berg, Erin A. Bohula, et al. "A pragmatic lab-based tool for risk assessment in cardiac critical care: data from the Critical Care Cardiology Trials Network (CCCTN) Registry." European Heart Journal. Acute Cardiovascular Care 11, no. 3 (February 3, 2022): 252–57. http://dx.doi.org/10.1093/ehjacc/zuac012.
Full textAbstract:
Abstract Aims Contemporary cardiac intensive care unit (CICU) outcomes remain highly heterogeneous. As such, a risk-stratification tool using readily available lab data at time of CICU admission may help inform clinical decision-making. Methods and results The primary derivation cohort included 4352 consecutive CICU admissions across 25 tertiary care CICUs included in the Critical Care Cardiology Trials Network (CCCTN) Registry. Candidate lab indicators were assessed using multivariable logistic regression. An integer risk score incorporating the top independent lab indicators associated with in-hospital mortality was developed. External validation was performed in a separate CICU cohort of 9716 patients from the Mayo Clinic (Rochester, MN, USA). On multivariable analysis, lower pH [odds ratio (OR) 1.96, 95% confidence interval (CI) 1.72–2.24], higher lactate (OR 1.40, 95% CI 1.22–1.62), lower estimated glomerular filtration rate (OR 1.26, 95% CI 1.10–1.45), and lower platelets (OR 1.18, 95% CI 1.05–1.32) were the top four independent lab indicators associated with higher in-hospital mortality. Incorporated into the CCCTN Lab-Based Risk Score, these four lab indicators identified a 20-fold gradient in mortality risk with very good discrimination (C-index 0.82, 95% CI 0.80–0.84) in the derivation cohort. Validation of the risk score in a separate cohort of 3888 patients from the Registry demonstrated good performance (C-index of 0.82; 95% CI 0.80–0.84). Performance remained consistent in the external validation cohort (C-index 0.79, 95% CI 0.77–0.80). Calibration was very good in both validation cohorts (r = 0.99). Conclusion A simple integer risk score utilizing readily available lab indicators at time of CICU admission may accurately stratify in-hospital mortality risk.
38
Medic, Goran, Melodi Kosaner Kließ, Louis Atallah, Jochen Weichert, Saswat Panda, Maarten Postma, and Amer EL-Kerdi. "Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review." F1000Research 8 (October 8, 2019): 1728. http://dx.doi.org/10.12688/f1000research.20498.1.
Full textAbstract:
Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.
39
Medic, Goran, Melodi Kosaner Kließ, Louis Atallah, Jochen Weichert, Saswat Panda, Maarten Postma, and Amer EL-Kerdi. "Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review." F1000Research 8 (November 27, 2019): 1728. http://dx.doi.org/10.12688/f1000research.20498.2.
Full textAbstract:
Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.
40
Wilson, Michael E., Ramona O. Hopkins, and Samuel M. Brown. "Long-Term Functional Outcome Data Should Not in General Be Used to Guide End-of-Life Decision-Making in the ICU." Critical Care Medicine 47, no. 2 (February 2019): 264–67. http://dx.doi.org/10.1097/ccm.0000000000003443.
Full text
41
Porter, Amanda L., James Ebot, Karen Lane, Lesia H. Mooney, Amy M. Lannen, Eugene M. Richie, Rachel Dlugash, et al. "Enhancing the Informed Consent Process Using Shared Decision Making and Consent Refusal Data from the CLEAR III Trial." Neurocritical Care 32, no. 1 (September 30, 2019): 340–47. http://dx.doi.org/10.1007/s12028-019-00860-y.
Full text
42
Pecanac, Kristen E., Shereen M. Massey, and Lindsey R. Repins. "How Patients and Families Describe Major Medical Treatments: “They are No Longer Living, Just Existing”." American Journal of Critical Care 31, no. 6 (November 1, 2022): 461–68. http://dx.doi.org/10.4037/ajcc2022705.
Full textAbstract:
Background As more life-sustaining treatments become available, the need to provide patients and families clarity about what these treatments are and what they do is increasing. Little is known about how patients and families conceptualize life support. Objective To explore the discourse that patients and families used to describe major medical treatments in their accounts of treatment decision-making. Methods This study is a secondary data analysis of a survey sent to random addresses in Wisconsin regarding experiences with major medical treatment decision-making. This analysis includes the subsample of 366 respondents who specified the type of decision made in the survey’s open-ended questions. Inductive content analysis was used to qualitatively analyze the responses to the open-ended questions, with particular attention to how respondents described the treatment in their responses. Results Respondents’ descriptions showed a conceptualization of engaging in major medical treatments as keeping patients alive, whereas discontinuing or choosing not to engage in such treatments would bring about the patient’s death. However, respondents recognized the potential adverse consequences of engaging in major medical treatments, such as their capacity to cause pain or result in an undesirable neurologic state. Additionally, respondents described the limitations of such treatment regarding the uncertainty of the treatments providing the desired outcome or their uselessness in situations in which the patient’s death would be inevitable. Conclusion Understanding how patients and families make sense of major medical treatments can help clinicians during decision-making conversations.
43
Bali, Jyoti, H. Arpitha, N. Anushree, and Arunkumar Giriyapur. "Power-efficient Strategies for Sensing in Autonomous Mobile Robots, a critical requirement of I4.0 standard." IOP Conference Series: Materials Science and Engineering 1187, no. 1 (September 1, 2021): 012007. http://dx.doi.org/10.1088/1757-899x/1187/1/012007.
Full textAbstract:
Abstract In a production environment, there are several challenges in meeting the Industry 4.0 (I4.0) standard requirements. Energy efficiency is an essential area of focus. In the production setup, the critical and real-time control systems need to be very efficient while implementing functions, namely, accurate sensing, fast processing and precise actuation. Automated Guided vehicles (AGVs) and Automated Guided Vehicles are an integral part of modern and intelligent manufacturing systems. Power consumption in such systems is directly proportional to the performance level achieved. However, there is a need to evolve strategies to reduce power consumption and attain optimal performance. Field Programmable Gate Array(FPGA) based controller solutions can provide competent performance at optimized power consumption. The proposed work discusses the requirements of I4.0 concerning energy efficiency infrastructures for the intelligent manufacturing setup. The need to develop efficient subsystems for sensing, decision-making and actuation based on FPGA is stressed. Thus the focus is on the FPGA based power-efficient models used for sensor fusion technique in Autonomous Mobile Robots. The fundamentals of sensor fusion technique and the need to fuse sensor data for improved decision making and actuation are emphasized.
44
Szatala, Amanda, and Bethany Young. "Implementation of a Data Acquisition and Integration Device in the Neurologic Intensive Care Unit." AACN Advanced Critical Care 30, no. 1 (March 15, 2019): 40–47. http://dx.doi.org/10.4037/aacnacc2019188.
Full textAbstract:
The neurologic intensive care unit has evolved into a data-rich, complex arena. Various neurologic monitors, collectively referred to as multimodality monitoring, provide clinicians with a plethora of real-time information about a comatose patient’s condition. The time and cognitive burden required to synthesize the available data and reach meaningful clinical conclusions can be overwhelming. The Moberg Component Neuromonitoring System (Moberg Research, Inc) is a data acquisition and integration device that collects data from multiple monitors, displaying them on a single screen in a way that highlights physiological trends throughout a patient’s clinical course. Implementation of the Moberg Component Neuromonitoring System in the neurologic intensive care unit can improve understanding of a patient’s neurophysiology, enhance clinical decision-making, and improve quality of care. Use of a staged process of implementation including exploration, installation, initial implementation, and full implementation can bring technology to the bedside in a sustainable fashion.
45
McNeilly, Dennis P., and Kristine Hillary. "The Hospice Decision: Psychosocial Facilitators and Barriers." OMEGA - Journal of Death and Dying 35, no. 2 (January 1, 1997): 193–217. http://dx.doi.org/10.2190/8w3n-q6uj-pxxw-q0l8.
Full textAbstract:
This study examined the social and contextual process of discussion and decision making around the use of the hospice in order to clarify the facilitative and obstructive aspects to hospice patient entry. Four participants groups of physicians, hospice and home health care patient family survivors, and hospice and home health care staff, completed four complementary mail surveys of their discussions and decision process for hospice care. Non-parametric analysis of the data reaffirmed the central and key role of the physician as the expected initiator and gatekeeper of the hospice discussion and decision-making process. Physicians were found generally aware of hospice and to have discussed hospice with their patients, though that awareness and the frequency of hospice patient discussions varied by the type of medical practice. Patient family survivors were unaware of hospice prior to the terminal illness, with a majority of hospice patient family survivors receiving their initial hospice information from relatives. Implications of these results and issues for future research are identified.
46
Kelly, Lesly A., Karen L. Johnson, R. Curtis Bay, and Michael Todd. "Key Elements of the Critical Care Work Environment Associated With Burnout and Compassion Satisfaction." American Journal of Critical Care 30, no. 2 (March 1, 2021): 113–20. http://dx.doi.org/10.4037/ajcc2021775.
Full textAbstract:
Background As the role of a health care system’s influence on nurse burnout becomes better understood, an under-standing of the impact of a nurses’ work environment on burnout and well-being is also imperative. Objective To identify the key elements of a healthy work environment associated with burnout, secondary trauma, and compassion satisfaction, as well as the effect of burnout and the work environment on nurse turnover. Methods A total of 779 nurses in 24 critical care units at 13 hospitals completed a survey measuring burnout and quality of the work environment. Actual unit-level data for nurse turnover during a 5-month period were queried and compared with the survey results. Results Among nurses in the sample, 61% experience moderate burnout. In models controlling for key nurse characteristics including age, level of education, and professional recognition, 3 key elements of the work environment emerged as significant predictors of burnout: staffing, meaningful recognition, and effective decision-making. The latter 2 elements also predicted more compassion satisfaction among critical care nurses. In line with previous research, these findings affirm that younger age is associated with more burnout and less compassion satisfaction. Conclusions Efforts are recommended on these 3 elements of the work environment (staffing, meaningful recognition, effective decision-making) as part of a holistic, systems-based approach to addressing burnout and well-being. Such efforts, in addition to supporting personal resilience-building activities, should be undertaken especially with younger members of the workforce in order to begin to address the crisis of burnout in health care.
47
Anderson, Rebecca J., Steven Bloch, Megan Armstrong, Patrick C. Stone, and Joseph TS Low. "Communication between healthcare professionals and relatives of patients approaching the end-of-life: A systematic review of qualitative evidence." Palliative Medicine 33, no. 8 (June 11, 2019): 926–41. http://dx.doi.org/10.1177/0269216319852007.
Full textAbstract:
Background: Effective communication between healthcare professionals and relatives of patients approaching the end-of-life is vital to ensure patients have a ‘good death’. To improve communication, it is important to first identify how this is currently being accomplished. Aim: To review qualitative evidence concerning characteristics of communication about prognosis and end-of-life care between healthcare professionals and relatives of patients approaching the end-of-life. Design: Qualitative systematic review (PROSPERO registration CRD42017065560) using thematic synthesis. Peer-reviewed, English language articles exploring the content of conversations and how participants communicated were included. No date restrictions were applied. Quality of included studies was appraised using the Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research. Data sources: An electronic database search of CINAHL, MEDLINE, PsycINFO and EMBASE was performed. Results: Thirty-one papers were included. Seven themes were identified: highlighting deterioration; involvement in decision-making, post-decision interactional work, tailoring, honesty and clarity, specific techniques for information delivery and roles of different healthcare professionals. Varied levels of family involvement in decision-making were reported. Healthcare professionals used strategies to aid understanding and collaborative decision-making, such as highlighting the patient’s deterioration, referring to patient wishes and tailoring information delivery. Doctors were regarded as responsible for discussing prognosis and decision-making, and nurses for providing individualized care. Conclusion: Findings suggest training could provide healthcare professionals with these strategies to improve communication. Interventions such as question prompt lists could help relatives overcome barriers to involvement in decision-making. Further research is needed to understand communication with relatives in different settings and with different healthcare professionals.
48
Jayatilake, Senerath Mudalige Don Alexis Chinthaka, and Gamage Upeksha Ganegoda. "Involvement of Machine Learning Tools in Healthcare Decision Making." Journal of Healthcare Engineering 2021 (January 27, 2021): 1–20. http://dx.doi.org/10.1155/2021/6679512.
Full textAbstract:
In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans. Implementing algorithms to perform such tasks itself is difficult, but what makes it even more challenging is to increase the accuracy of the algorithm while decreasing the required time for the algorithm to execute. In the early days, processing of large amount of medical data was an important task which resulted in machine learning being adapted in the biological domain. Since this happened, the biology and biomedical fields have been reaching higher levels by exploring more knowledge and identifying relationships which were never observed before. Reaching to its peak now the concern is being diverted towards treating patients not only based on the type of disease but also their genetics, which is known as precision medicine. Modifications in machine learning algorithms are being performed and tested daily to improve the performance of the algorithms in analysing and presenting more accurate information. In the healthcare field, starting from information extraction from medical documents until the prediction or diagnosis of a disease, machine learning has been involved. Medical imaging is a section that was greatly improved with the integration of machine learning algorithms to the field of computational biology. Nowadays, many disease diagnoses are being performed by medical image processing using machine learning algorithms. In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making. Throughout this paper, various machine learning algorithms and approaches that are being used for decision making in the healthcare sector will be discussed along with the involvement of machine learning in healthcare applications in the current context. With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power of modern sophisticated computers and are being extensively applied because of their high predicting accuracy and reliability. When giving concern towards the big picture by combining the observations, it is noticeable that computational biology and biomedicine-based decision making in healthcare have now become dependent on machine learning algorithms, and thus they cannot be separated from the field of artificial intelligence.
49
Wu, Yunfeng, Sridhar Krishnan, and Behnaz Ghoraani. "Computational Methods for Physiological Signal Processing and Data Analysis." Computational and Mathematical Methods in Medicine 2022 (August 10, 2022): 1–4. http://dx.doi.org/10.1155/2022/9861801.
Full textAbstract:
Biomedical signal processing and data analysis play pivotal roles in the advanced medical expert system solutions. Signal processing tools are able to diminish the potential artifact effects and improve the anticipative signal quality. Data analysis techniques can assist in reducing redundant data dimensions and extracting dominant features associated with pathological status. Recent computational methods have greatly improved the effectiveness of signal processing and data analysis, to support the efficient point-of-care diagnosis and accurate medical decision-making. This editorial article highlights the research works published in the special issue of Computational Methods for Physiological Signal Processing and Data Analysis. The context introduces three deep learning applications in epileptic seizure detection, human exercise intensity analysis, and lung nodule CT image segmentation, respectively. The article also summarizes the research works on detection of event-related potential in the single-trial electroencephalogram (EEG) signals during the auditory tests, along with the methodology on estimating the generalized exponential distribution parameters using the simulated and real data produced under the Type I generalized progressive hybrid censoring schemes. The article concludes with perspectives and discussions on future trends in biomedical signal processing and data analysis technologies.
50
Abraham Isiaho, Kelvin Kabeti Omieno, and Hillan Rono. "Tele-care medical information systems security techniques: A critical review of the state of the art techniques." World Journal of Advanced Engineering Technology and Sciences 7, no. 2 (December 30, 2022): 240–54. http://dx.doi.org/10.30574/wjaets.2022.7.2.0136.
Full textAbstract:
The advancement in information communication technologies has seen the rise in the deployment of various information exchange devices in the healthcare sector. Among these technologies is the Tele-care Medical Information Systems (TMIS) in which remote users can establish a connection with the hospital medical server and share the necessary information between them. This can potentially offer doctors and patients more reasonable treatment plan, as well as helping address the huge medical expenses and excessive medical treatment duration. There is therefore need to store patient data in the end devices, as well as transmit this data over public channels to facilitate decision making. This paper sought to review the security schemes that have been developed over the recent past to protect the patient data stored or transmitted in TMIS.