Public, patient and carers’ views on palliative and end‐of‐life care in India

Venkatasalu, Munikumar Ramasamy; Jagadeesh, Nalini Sirala; Elavally, Sujitha; Pappas, Yannis; Mhlanga, Fortune; Varatharajan, R.

doi:10.1111/inr.12403

Cited by 12 publications

(11 citation statements)

References 29 publications

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“…3 Leveraging ML algorithms within EHRs offer promising opportunities to address the challenges in PC referral triggers, which could include diagnosis of life-limiting or terminal illnesses (e.g., advanced cancer, end-stage heart failure), severe and progressive symptoms (e.g., uncontrolled pain, dyspnea), frequent hospitalizations and readmissions, decline in functional status and quality of life, and limited response to curative treatments. [4][5][6][7][8] Some potential benefits include early identification and intervention by analyzing patient data available in EHRs, objective and standardized referral criteria, enhanced accuracy and efficiency, and personalized care planning as ML models can consider a wide range of patient characteristics, such as medical history, comorbidities, and PC needs assessment like physical assessment of symptoms (pain, nausea, fatigue), psychosocial assessment (emotional distress, spiritual concerns), discussion of patient's goals, values, and preferences, and assessment of family and caregiver needs to develop personalized care plans. 2,9 Clinical Use Case Design…”

Section: Overview Of the Current Challenges In Pc Referrals And The P...mentioning

confidence: 99%

Enhancing Hospitalized Patients' Palliative Care Referrals via Machine Learning-Based Predictive Modeling within Electronic Health Record Systems

Ghoshal

2024

Indian J Med Paediatr Oncol

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Access to palliative care (PC) holds significance for hospital-admitted patients grappling with the symptoms of life limiting illnesses. Nonetheless, numerous such patients who could gain from PC fail to receive it promptly or even at all.We can leverage the prior year's historical data extracted from electronic health records of hospitalized patients to train a machine learning (ML) model. This model's purpose would be to prognosticate the requirement for PC consultation using real-time data. The model, operating as a semi-supervised system, will be integrated into institutional data pipelines, and utilized by a downstream display application overseen by the PC team. In cases where the PC team deems it suitable, a team member will communicate with the respective care team of the patient. The ML model's training efficacy will be assessed using the area under the curve (AUC) metric, employing a 20% reserved validation set. The threshold for PC consultations will be grounded in historical data. To enhance the ML model's precision, the pivotal variables within the model will be pinpointed, and any sources of biases or errors in the model will be identified for meticulous refinement. The AUC values of successive ML models will be juxtaposed with cross-validation data.Automatizing the referral procedure through electronic health record systems has the potential to usher in a more effective and streamlined approach to healthcare delivery.

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Section: Overview Of the Current Challenges In Pc Referrals And The P...mentioning

confidence: 99%

Enhancing Hospitalized Patients' Palliative Care Referrals via Machine Learning-Based Predictive Modeling within Electronic Health Record Systems

Ghoshal

2024

Indian J Med Paediatr Oncol

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“…No caso de pacientes terminais a assistência de enfermagem deve explorar as necessidades físicas, espirituais, psicossociais do indivíduo, estando atenta a preferência do local da morte, a situação domiciliar do paciente e as necessidades informais dos familiares, como a utilização de mantras, água benta, amuletos, fotos de seu Deus; geralmente utilizados como forma de receber força em períodos de sofrimento (Venkatasalu et al, 2018).…”

Section: Condutas De Enfermagem Correlacionadas Com O Tratamento E/ou Terapia Cultural Religiosaunclassified

Benefícios das práticas religiosas na assistência de enfermagem: Uma revisão integrativa

Araujo

Silva

Vilanova

et al. 2021

RSD

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Desde os primórdios da ciência do cuidado ao ser humano, as práticas religiosas e a espiritualidade enfrentam um olhar rigoroso dos cientistas e pesquisadores. A assistência de enfermagem deve prestar cuidados e medidas de segurança ao indivíduo sob um modelo de unidade biopsicossocial, em todos os aspectos físicos, sociais, econômicos, espirituais e culturais. Nesse caminho, este artigo teve como objetivo geral analisar as produções científicas acerca dos benefícios das práticas religiosas na prestação de cuidados de enfermagem, e especificamente descrever a importância das práticas religiosas no estado de saúde, destacar as principais limitações e os desafios nos cuidados de enfermagem com as práticas de cura cultural-religiosa e relacionar as condutas de enfermagem com o tratamento e/ou terapia cultural/religiosa. Trata-se de uma revisão integrativa da literatura, na qual se formulou a questão: "Quais os benefícios da associação entre as práticas religiosas e assistência de enfermagem na prestação de cuidados?". Com uso de descritores e palavras-chaves, foram consultadas as bases de dados Bireme, CINAHL e PubMed. Utilizou-se os estudos disponíveis em totalidade, publicados nos anos de 2015 até 2020, nos idiomas Inglês, Português e Espanhol. Os artigos apresentaram que religiosidade e espiritualidade são dimensões fundamentais nos processos de cura, reabilitacionais e de superação, e devem ser implementadas na ciência do cuidado pelos profissionais de saúde. Concluiu-se que é dever da enfermagem e de outras áreas da saúde, atender o indivíduo com assistência humanizada, respeitando suas necessidades espiritual-religiosas no tratamento e/ou terapia.

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“…Data reduction techniques utilise the process of coding the data in order to 'simplify, abstract, focus and organise it into manageable features' (Ramasamy Venkatasalu et al, 2017). The study adopted subgrouping and single page tabulation as valid and reliable coding techniques in order to enhance methodological rigour (Broome, 2000, Brown et al, 2003.…”

Section: Data Reductionmentioning

confidence: 99%

Predictors for nurses and midwives' readiness towards self-directed learning: An integrated review

Chakkaravarthy

Ibrahim

Mahmud

et al. 2018

Nurse Education Today

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The review concludes that qualified nurses and midwives have a unique SDL predictors and process; hence, a personalized SDL programme should be prescribed based on personality traits so as to achieve better SDL outcomes. Future research should address the facilitating factors for SDLR, barriers to SDLR and strategies to improve SDLR among nurses and more importantly midwives, as limited evidence is available with respect to the latter.

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Public, patient and carers’ views on palliative and end‐of‐life care in India

Cited by 12 publications

References 29 publications

Enhancing Hospitalized Patients' Palliative Care Referrals via Machine Learning-Based Predictive Modeling within Electronic Health Record Systems

Enhancing Hospitalized Patients' Palliative Care Referrals via Machine Learning-Based Predictive Modeling within Electronic Health Record Systems

Benefícios das práticas religiosas na assistência de enfermagem: Uma revisão integrativa

Predictors for nurses and midwives' readiness towards self-directed learning: An integrated review

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