Methodological Background of Decision Rules and Feedback Tools for Outcomes Management in Psychotherapy

Systems to provide feedback regarding treatment progress have been recognized as a promising method for the early identification of patients at risk for treatment failure in outpatient psychotherapy. The feedback systems presented in this article rely on decision rules to contrast the actual treatment progress of an individual patient and his or her expected treatment response (ETR). Approaches to predict the ETR on the basis of patient intake characteristics and previous treatment progress can be classified into two broad classes: Rationally derived decision rules rely on the judgments of experts, who determine the amount of progress that a patient has to achieve for a given treatment session to be considered “on track.” Empirically derived decision rules are based on expected recovery curves derived from statistical models applied to aggregated psychotherapy outcomes data. Examples of each type of decision rule and of feedback systems based on such rules are presented and reviewed

Does Culture Influence the Needs of Critical Care Families?

Purpose: This study explores ICU patient\u27s family member needs, particularly Vietnamese and Latino families. Design: Convenience sampling at 24 bed ICU in acute care community hospital serving ethnically diverse population. Methods: Non-experimental survey with pretest-posttest design using Demographic sheet, Critical Family Needs Inventory (CCFNI), and Needs Met Inventory (NMI). Information pamphlets were distributed. Data analysis was by ethnic groups using measures of central tendency and descriptive statistics. Findings: CCFNI results indicate family members of all ethnicities experience the same priority of needs; support and information are top two needs. English, Spanish and Vietnamese pamphlets met information needs of the majority of the recipients

Automated Home Oxygen Delivery for Patients with COPD and Respiratory Failure: A New Approach

Long-term oxygen therapy (LTOT) has become standard care for the treatment of patients with chronic obstructive pulmonary disease (COPD) and other severe hypoxemic lung diseases. The use of new portable O-2 concentrators (POC) in LTOT is being expanded. However, the issue of oxygen titration is not always properly addressed, since POCs rely on proper use by patients. The robustness of algorithms and the limited reliability of current oximetry sensors are hindering the effectiveness of new approaches to closed-loop POCs based on the feedback of blood oxygen saturation. In this study, a novel intelligent portable oxygen concentrator (iPOC) is described. The presented iPOC is capable of adjusting the O-2 flow automatically by real-time classifying the intensity of a patient's physical activity (PA). It was designed with a group of patients with COPD and stable chronic respiratory failure. The technical pilot test showed a weighted accuracy of 91.1% in updating the O-2 flow automatically according to medical prescriptions, and a general improvement in oxygenation compared to conventional POCs. In addition, the usability achieved was high, which indicated a significant degree of user satisfaction. This iPOC may have important benefits, including improved oxygenation, increased compliance with therapy recommendations, and the promotion of PA

Towards defining semantic foundations for purpose-based privacy policies

We define a semantic model for purpose, based on which purpose-based privacy policies can be meaningfully expressed and enforced in a business system. The model is based on the intuition that the purpose of an action is determined by its situation among other inter-related actions. Actions and their relationships can be modeled in the form of an action graph which is based on the business processes in a system. Accordingly, a modal logic and the corresponding model checking algorithm are developed for formal expression of purpose-based policies and verifying whether a particular system complies with them. It is also shown through various examples, how various typical purpose-based policies as well as some new policy types can be expressed and checked using our model

Accessing Patient Records in Virtual Healthcare Organisations

The ARTEMIS project is developing a semantic web service based P2P interoperability infrastructure for healthcare information systems that will allow healthcare providers to securely share patient records within virtual healthcare organisations. Authorisation decisions to access patient records across organisation boundaries can be very dynamic and must occur within a strict legislative framework. In ARTEMIS we are developing a dynamic authorisation mechanism called PBAC that provides a means of contextual and process oriented access control to enforce healthcare business processes. PBAC demonstrates how healthcare providers can dynamically share patient records for care pathways across organisation boundaries

Multi-Factor Triage Algorithm (MUFTA): Quantitative and Qualitative Ethical Factors on Triage Decisions During COVID-19

Background: This study shows how multiple ethical criteria evaluations result in patient screening and ranking. Furthermore, as Omicron outbreaks increase, hospital emergency departments will become overburdened with critically ill patients. It is a one-of-a-kind global triage algorithm for infectious decreases of COVID-19 and Omicron. The algorithm is qualitative and quantitative, and adaptable to various bio-ethical and social factors. The measurement of the evaluation process eliminates any inconsistencies, which is an advantage of a decision-making algorithm. The proposed algorithm is unique because there are no similar algorithms in the literature that provide triage guidelines based on social ethics, bioethics, and human dignity. Objective: It's simple to evaluate a patient's potential benefits when ethical triage judgments are structured and transparent. Furthermore, decisions made primarily based on economic considerations in stressful situations overlook the socioeconomic realities of the underprivileged. This triage algorithm eliminates the need for ad hoc triage evaluations and facilitates criteria for inclusion, such as human dignity. It also takes into account patient comorbidities and social, ethical issues. Method: Healthcare professionals use predefined ethical criteria to assign relative rankings among patients based on treatment response and social circumstances. It is a Delphi method for evaluating patient illnesses with the help of medical professionals. For example, the admission to the intensive care unit and providing a ventilator depend entirely on hierarchical multidimensional triage scoring results. This algorithm can evaluate triage scores quickly. It is robust, accurate, and quick in assessment, evaluation, and reevaluation during an emergency. A team of three experts can implement this algorithm. Result: The Consistency Scores (CR) show how well clinical and non-clinical ethical criteria may be used to make triage judgments. As a result, all specialists have reported allogeneic reactions in the triage assessment. Furthermore, this system enables decision-makers to identify cognitive biases that may influence their decisions. A Group Consciousness Ratio (GCR) of over 85% indicates that the decision-making process is transparent. Patients with a high level of social dependency, a reasonable probability of recovery, a favorable weighted average comorbidity score, and those who are less fortunate are all considered in the overall triage decision. Conclusions: This algorithm differentiates patients who need ICU (Incentive Care Unit) care and do not immediately require critical resources. As a result, patients queue up on a waiting list when the ICU demand spikes due to the increased incidence of COVID-19 infection or its variants. This situation presents a dilemma for the triage policy. Therefore, a national emergency policy requires monetary and technical assistance to expand healthcare facilities. However, the clarity of this triage policymaking is at odds with decision-makers interested in manipulating results. It is challenging to deal with consistency issues in the Delphi process in group decision-making without professional moderators and valid evaluation metrics. Therefore, transparency, consistency, and strong judgment are essential elements of the presented algorithm. Doi: 10.28991/esj-2022-SPER-07 Full Text: PD

A model for continuous monitoring of patients with major depression in short and long term periods

The final publication is available at IOS Press through http://dx.doi.org/10.3233/THC-161289BACKGROUND AND OBJECTIVE: Major depressive disorder causes more human suffering than any other disease affecting humankind. It has a high prevalence and it is predicted that it will be among the three leading causes of disease burden by 2030. The prevalence of depression, all of its social and personal costs, and its recurrent characteristics, put heavy constraints on the ability of the public healthcare system to provide sufficient support for patients with depression. In this research, a model for continuous monitoring and tracking of depression in both short-term and long-term periods is presented. This model is based on a new qualitative reasoning approach. METHOD: This paper describes the patient assessment unit of a major depression monitoring system that has three modules: a patient progress module, based on a qualitative reasoning model; an analysis module, based on expert knowledge and a rules-based system; and the communication module. These modules base their reasoning mainly on data of the patient's mood and life events that are obtained from the patient's responses to specific questionnaires (PHQ-9, M.I.N.I. and Brugha). The patient assessment unit provides synthetic and useful information for both patients and physicians, keeps them informed of the progress of patients, and alerts them in the case of necessity. RESULTS: A set of hypothetical patients has been defined based on clinically possible cases in order to perform a complete scenario evaluation. The results that have been verified by psychiatrists suggest the utility of the platform. CONCLUSION: The proposed major depression monitoring system takes advantage of current technologies and facilitates more frequent follow-up of the progress of patients during their home stay after being diagnosed with depression by a psychiatrist.Peer ReviewedPostprint (author's final draft