Toward shared decision-making in degenerative cervical myelopathy: Protocol for a mixed methods study

Health care decisions are a critical determinant in the evolution of chronic illness. In shared decision-making (SDM), patients and clinicians work collaboratively to reach evidence-based health decisions that align with individual circumstances, values, and preferences. This personalized approach to clinical care likely has substantial benefits in the oversight of degenerative cervical myelopathy (DCM), a type of nontraumatic spinal cord injury. Its chronicity, heterogeneous clinical presentation, complex management, and variable disease course engenders an imperative for a patient-centric approach that accounts for each patient's unique needs and priorities. Inadequate patient knowledge about the condition and an incomplete understanding of the critical decision points that arise during the course of care currently hinder the fruitful participation of health care providers and patients in SDM. This study protocol presents the rationale for deploying SDM for DCM and delineates the groundwork required to achieve this. The study's primary outcome is the development of a comprehensive checklist to be implemented upon diagnosis that provides patients with essential information necessary to support their informed decision-making. This is known as a core information set (CIS). The secondary outcome is the creation of a detailed process map that provides a diagrammatic representation of the global care workflows and cognitive processes involved in DCM care. Characterizing the critical decision points along a patient's journey will allow for an effective exploration of SDM tools for routine clinical practice to enhance patient-centered care and improve clinical outcomes. Both CISs and process maps are coproduced iteratively through a collaborative process involving the input and consensus of key stakeholders. This will be facilitated by Myelopathy.org, a global DCM charity, through its Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy community. To develop the CIS, a 3-round, web-based Delphi process will be used, starting with a baseline list of information items derived from a recent scoping review of educational materials in DCM, patient interviews, and a qualitative survey of professionals. A priori criteria for achieving consensus are specified. The process map will be developed iteratively using semistructured interviews with patients and professionals and validated by key stakeholders. Recruitment for the Delphi consensus study began in April 2023. The pilot-testing of process map interview participants started simultaneously, with the formulation of an initial baseline map underway. This protocol marks the first attempt to provide a starting point for investigating SDM in DCM. The primary work centers on developing an educational tool for use in diagnosis to enable enhanced onward decision-making. The wider objective is to aid stakeholders in developing SDM tools by identifying critical decision junctures in DCM care. Through these approaches, we aim to provide an exhaustive launchpad for formulating SDM tools in the wider DCM community. DERR1-10.2196/46809. [Abstract copyright: ©Irina Sangeorzan, Grazia Antonacci, Anne Martin, Ben Grodzinski, Carl M Zipser, Rory K J Murphy, Panoraia Andriopoulou, Chad E Cook, David B Anderson, James Guest, Julio C Furlan, Mark R N Kotter, Timothy F Boerger, Iwan Sadler, Elizabeth A Roberts, Helen Wood, Christine Fraser, Michael G Fehlings, Vishal Kumar, Josephine Jung, James Milligan, Aria Nouri, Allan R Martin, Tammy Blizzard, Luiz Roberto Vialle, Lindsay Tetreault, Sukhvinder Kalsi-Ryan, Anna MacDowall, Esther Martin-Moore, Martin Burwood, Lianne Wood, Abdul Lalkhen, Manabu Ito, Nicky Wilson, Caroline Treanor, Sheila Dugan, Benjamin M Davies. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 09.10.2023.

“I attend at Vanguard and I attend here as well”: barriers to accessing healthcare services among older South Africans with HIV and non-communicable diseases

Background: HIV and non-communicable disease (NCD) are syndemic within sub-Saharan Africa especially among older persons. The two epidemics interact with one another within a context of poverty, inequality and inequitable access to healthcare resulting in an increase in those aged 50 and older living with HIV and experiencing an NCD comorbidity. We explore the challenges of navigating healthcare for older persons living with HIV and NCD co-morbidity. Methods: In-depth semi-structured interviews were conducted with a small sample of older persons living with HIV (OPLWH). The perspectives of key informants were also sought to triangulate the evidence of OPLWH. The research took place in two communities on the outskirts of Cape Town, South Africa. All interviews were conducted by a trained interviewer and transcribed and translated for analysis. Thematic content analysis guided data analysis. Results: OPLWH experienced an HIV-NCD syndemic. Our respondents sought care and accessed treatment for both HIV and other chronic (and acute) conditions, though these services were provided at different health facilities or by different health providers. Through the syndemic theory, it is possible to observe that OPLWH and NCDs face a number of physical and structural barriers to accessing the healthcare system. These barriers are compounded by separate appointments and spaces for each condition. These difficulties can exacerbate the impact of their ill-health and perpetuate structural vulnerabilities. Despite policy changes towards integrated care, this is not the experience of OPLWH in these communities. Conclusions: The population living with HIV is aging increasing the likelihood that those living with HIV will also be living with other chronic conditions including NCDs. Thus, it is essential that health policy address this basic need to integrate HIV and NCD care

Multimorbidity in non-communicable diseases in South African primary healthcare

CITATION: Lalkhen, H. & Mash, R. 2015. Multimorbidity in non-communicable diseases in South African primary healthcare. South African Medical Journal, 105(2):134-138, doi:10.7196/SAMJ.8696.The original publication is available at http://www.samj.org.zaBackground: Multimorbidity in non-communicable diseases (NCDs) is a complex global healthcare challenge that is becoming increasingly prevalent. In Africa, comorbidity of communicable diseases and NCDs is also increasing. Objectives: To evaluate the extent of multimorbidity among patients with NCDs in South African (SA) primary healthcare (PHC). Methods: A dataset obtained from a previous morbidity survey of SA ambulatory PHC was analysed. Data on conditions considered active and ongoing at consultations by PHC providers were obtained. Results: Altogether 18 856 consultations were included in the dataset and generated 31 451 reasons for encounter and 24 561 diagnoses. Hypertension was the commonest NCD diagnosis encountered (13.1%), followed by type 2 diabetes (3.9%), osteoarthritis (2.2%), asthma (2.0%), epilepsy (1.9%) and chronic obstructive pulmonary disease (COPD) (0.6%). The majority of patients (66.9%) consulted a nurse and 33.1% a doctor. Overall 48.4% of patients had comorbidity and 14.4% multimorbidity. Multimorbidity (two or more conditions) was present in 36.4% of patients with COPD, 23.7% with osteoarthritis, 16.3% with diabetes, 15.3% with asthma, 12.0% with hypertension and 6.7% with epilepsy. Only 1.1% also had HIV, 1.0% TB, 0.4% depression and 0.04% anxiety disorders. Conclusion: About half of the patients with NCDs had comorbidity, and multimorbidity was common in patients with COPD and osteoarthritis. However, levels of multimorbidity were substantially lower than reported in higher-income countries. Future clinical guidelines, training of PHC nurses and involvement of doctors in the continuum of care should address the complexity of patients with NCDs and multimorbidity.http://www.samj.org.za/index.php/samj/article/view/8696Publisher's versio

Time warped continuous speech signal matching using Kalman filter

NoDynamic speech properties, such as time warping, silence removal and background noise reduction are the most challenging issues in continuous speech signal matching. Among all of them, the time warped speech signal matching is of great interest and has been a tough challenge for the researchers. The literature contains a variety of techniques to measure the similarity between speech utterances, however there are some limitations associated with these techniques. This paper introduces an adaptive framing based continuous speech tracking and similarity measurement approach that uses a Kalman filter (KF) as a robust tracker. The use of KF is novel for time warped speech signal matching and dynamic time warping. A dynamic state model is presented based on equations of linear motion. In this model, fixed length frame of input (test) speech signal is considered as a unidirectional moving object by sliding it along the template speech signal. The best matched position estimate in template speech (sample number) for corresponding test frame at current time is calculated. Simultaneously, another position observation is produced by a feature based distance metric. The position estimated by the state model is fused with the observation using KF along with the noise variances. The best estimated frame position in the template speech for the current state is calculated. Finally, forecasting of the noise variances and template frame size for next state are made according to the KF output. The experimental results demonstrate the robustness of the proposed technique in terms of time warped speech signal matching as well as in computation cost

eWound-PRIOR: An Ensemble Framework for Cases Prioritization After Orthopedic Surgeries

© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. Patient follow-up appointments are an imperative part of the healthcare model to ensure safe patient recovery and proper course of treatment. The use of mobile devices can help patient monitoring and predictive approaches can provide computational support to identify deteriorating cases. Aiming to aggregate the data produced by those devices with the power of predictive approaches, this paper proposes the eWound-PRIOR framework to provide a remote assessment of postoperative orthopedic wounds. Our approach uses Artificial Intelligence (AI) techniques to process patients’ data related to postoperative wound healing and makes predictions as to whether the patient requires an in-person assessment or not. The experiment results showed that the predictions are promising and adherent to the application context, even if the on-line questionnaire had impaired the training model and the performance