
==== Front
Indian J Crit Care Med
Indian J Crit Care Med
IJCCM
Indian Journal of Critical Care Medicine : Peer-reviewed, Official Publication of Indian Society of Critical Care Medicine
0972-5229
1998-359X
Jaypee Brothers Medical Publishers

39130387
10.5005/jp-journals-10071-24725
Original Article
End-of-life Care Patient Information Leaflets—A Comparative Evaluation of Artificial Intelligence-generated Content for Readability, Sentiment, Accuracy, Completeness, and Suitability: ChatGPT vs Google Gemini
Gondode Prakash G 1https://orcid.org/0000-0003-1014-8407

Khanna Puneet 2https://orcid.org/0000-0002-9243-9963

Sharma Pradeep 3https://orcid.org/0009-0005-4875-0550

Duggal Sakshi 4https://orcid.org/0000-0002-8865-0854

Garg Neha 5https://orcid.org/0000-0003-4817-9807

1,2,4,5 Department of Anesthesiology, Pain Medicine and Critical Care, All India Institute of Medical Sciences, New Delhi, India
3 Department of Critical Care Medicine, NH Hospital, Raipur, Chhattisgarh, India
Prakash G Gondode, Department of Anesthesiology, Pain Medicine and Critical Care, All India Institute of Medical Sciences, New Delhi, India, Phone: +91 8826434672, e-mail: drprakash777@gmail.com
6 2024
31 5 2024
28 6 561568
11 3 2024
30 4 2024
Copyright © 2024; The Author(s).
2024
https://creativecommons.org/licenses/by-nc/4.0/ © The Author(s). 2024 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and non-commercial reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Abstract

Background

End-of-life care (EOLC) is a critical aspect of healthcare, yet accessing reliable information remains challenging, particularly in culturally diverse contexts like India.

Objective

This study investigates the potential of artificial intelligence (AI) in addressing the informational gap by analyzing patient information leaflets (PILs) generated by AI chatbots on EOLC.

Methodology

Using a comparative research design, PILs generated by ChatGPT and Google Gemini were evaluated for readability, sentiment, accuracy, completeness, and suitability. Readability was assessed using established metrics, sentiment analysis determined emotional tone, accuracy, and completeness were rated by subject experts, and suitability was evaluated using the Patient Education Materials Assessment Tool (PEMAT).

Results

Google Gemini PILs exhibited superior readability and actionability compared to ChatGPT PILs. Both conveyed positive sentiments and high levels of accuracy and completeness, with Google Gemini PILs showing slightly lower accuracy scores.

Conclusion

The findings highlight the promising role of AI in enhancing patient education in EOLC, with implications for improving care outcomes and promoting informed decision-making in diverse cultural settings. Ongoing refinement and innovation in AI-driven patient education strategies are needed to ensure compassionate and culturally sensitive EOLC.

How to cite this article

Gondode PG, Khanna P, Sharma P, Duggal S, Garg N. End-of-life Care Patient Information Leaflets—A Comparative Evaluation of Artificial Intelligence-generated Content for Readability, Sentiment, Accuracy, Completeness, and Suitability: ChatGPT vs Google Gemini. Indian J Crit Care Med 2024;28(6):561–568.

Keywords

Artificial intelligence
ChatGPT
End-of-life care
Gemini
Intensive care
Palliative care
Patient education
Readability
==== Body
pmcStudy Highlights

Findings emphasize AI's promising role in end-of-life care (EOLC) patient education.

Both ChatGPT and Google Gemini patient information leaflets (PILs) foster hope in EOLC, with Gemini PILs being more readable.

Both excel in accuracy and completeness, offering reliable EOLC information.

Introduction

End-of-life care (EOLC) represents a critical aspect of healthcare that warrants careful attention and understanding. It involves providing comprehensive support and comfort to individuals nearing the end of their lives, as well as their families and caregivers.1 However, despite its importance, EOLC often lacks reliable and accessible information, leading to confusion and uncertainty among individuals and families facing end-of-life issues. The proficiency of a healthcare system in effectively addressing end-of-life scenarios appears contingent upon several straightforward, cost-effective, and technologically simple factors. Chief among these considerations is the degree of public awareness and the integration of discussions surrounding death (naturalization) into routine discourse.2

This scarcity of trustworthy information is further compounded by cultural, religious, and socioeconomic factors, particularly in countries like India. India ranks 67th among 80 countries in the world on the quality of death index, making India ‘not a country to die in’.3 Here, diverse cultural norms, language barriers, and disparities in healthcare access make navigating EOLC complexities even more challenging. As a result, there is a pressing need for culturally sensitive and contextually relevant patient education initiatives to address the informational needs of diverse populations.4

Effective patient education in EOLC has the potential to transform attitudes, reduce stigma, and promote dignity and compassion. By providing accurate, understandable, and culturally sensitive information, patient education initiatives empower individuals and families to make informed decisions and actively participate in their care.5 Moreover, fostering open and honest conversations about death and dying creates a supportive environment that enhances the quality of life during the end-of-life journey.

In this context, the emergence of AI technologies offers promising opportunities to enhance patient education and support in EOLC. AI-powered platforms, such as ChatGPT and Google Gemini, can generate PILs tailored to specific healthcare topics, including EOLC.6 Evaluating the effectiveness and suitability of AI-generated PILs in providing accurate, understandable, and culturally relevant information on EOLC is crucial for advancing patient education and improving EOLC outcomes in diverse healthcare settings.

Therefore, this study aims to undertake a comprehensive analysis of AI-generated PILs on EOLC, comparing PILs generated by ChatGPT and Google Gemini. By examining factors such as readability, sentiment, accuracy, completeness, and suitability, we seek to elucidate the strengths and limitations of AI-generated PILs in addressing the informational needs of individuals and families facing the end of life. Through our findings, we hope to contribute to the ongoing efforts to enhance patient education and support in EOLC, ultimately striving towards a more compassionate and informed approach to end-of-life care in diverse cultural contexts.

Methodology

This study compared and analyzed two PILs generated by AI chatbots like ChatGPT and Google Gemini, focusing on the topic of EOLC.

Study Design

The study adopted a comparative research design, aiming to examine and compare the differences between the ChatGPT and Google Gemini PILs. This design allowed for a systematic assessment of various aspects of the PILs, including readability, sentiment, accuracy, completeness, and suitability.

Selection of PILs

The PILs generated by ChatGPT and Google Gemini were purposefully selected based on their relevance to the topic of End-of-life care, by giving a specific prompt, ‘generate a PIL patient information leaflet about educating the general population about EOLC, use appropriate icons or clip arts’. Both PILs were retrieved from their respective platforms and anonymized to maintain confidentiality and impartiality during analysis. Artificial intelligence (AI) chatbot responses vary based on the prompts, and generative AI can produce different responses with the same prompts. In this study, we opted to assess only the initial output from each AI chatbot. This approach mirrors real-world scenarios where users typically interact with the first response provided. Additionally, it offers a practical and manageable method for evaluating AI chatbot performance within the context of the research question.

Blinded Annotation

To ensure impartial evaluation, the collected materials are anonymized and assigned unique identifiers. Expert reviewers are blinded to the source of each material, with Annexure A designated for the evaluation of AI ChatGPT content and Annexure B for Gemini content.

Evaluation Measures

Readability Tests

To evaluate the readability of the PILs, established metrics such as Flesch Reading Ease, Gunning Fog Index, Flesch-Kincaid Grade Level, SMOG Index and were utilized. These metrics provided quantitative measures of the PILs’ ease of comprehension.

Sentiment Analysis

A sentiment analysis was conducted to assess the emotional tone and sentiment conveyed by each PIL. This analysis offered insights into the overall sentiment expressed within the PILs.

Accuracy and Completeness Scores

Subject matter experts (five), experienced in the field of intensive care medicine with at least five and above years of experience, and regularly involved in EOLC, evaluated the accuracy and completeness of both PILs using predefined criteria and a structured scoring system. On Likert scale of 1–5. Accuracy focused on the factual correctness of information presented, while completeness assessed the extent to which essential information was covered.

The Patient Education Materials Assessment Tool (PEMAT) Analysis

A PEMAT analysis was conducted to evaluate the suitability of the PILs for their intended audience. This analysis assessed the understandability and actionability of the PILs, providing valuable insights into their effectiveness in communicating information and facilitating decision-making.

Data Collection and Analysis

The readability scores for each PIL were computed using the appropriate formulas for FRE, GFI, and FKGL. These scores were compared to identify any discrepancies in readability between the ChatGPT and Google Gemini PILs. Generating sentiment scores for comparative analysis. Subject matter experts independently evaluated the accuracy and completeness of both PILs based on predefined criteria. Mean scores and standard deviations were calculated and compared for each PIL. The PEMAT analysis assessed the understandability and actionability of the PILs, with scores calculated for each component to discern differences in suitability between the ChatGPT and Google Gemini PILs.

Statistical Analysis

Where applicable, statistical tests such as t-tests or ANOVA were utilized to determine significant differences between the PILs across various dimensions, including readability, sentiment, accuracy, completeness, and suitability.

Websites and software used: For generating text, we used the chatbots of ChatGPT 3.5, freely available in the public domain, available at https:// https://chat.openai.com/ and Google Gemini available at https:// https://gemini.google.com/app. For PEMAT-P analysis, https://www.ahrq.gov/health-literacy/patient-education/pemat-p.html. For readability, we used the online calculator available at https://readable.com/ and https://readabilityformulas.com/readability-scoring-system.php. For calculating the sentiment score we used, https://monkeylearn.com/sentiment-analysis-online/, and for calculating statistics, Microsoft Excel and online software available at https://www.socscistatistics.com/tests/anova/default2.aspx.

Ethical Considerations

Given that this study solely involved the analysis of pre-existing educational materials generated by AI chatbots and did not involve any human subjects or data that required ethical approval, formal ethical approval was not deemed necessary for this research. The study adhered to ethical standards by ensuring the confidentiality and anonymity of collected data and by conducting the analysis in a transparent and unbiased manner. Additionally, no personal or sensitive information was collected or used in the study, further mitigating the need for ethical approval.

Results

In this study, we compared the two patient information leaflets (PILs) generated by ChatGPT and Google Gemini on the topic of EOLC. We evaluated the PILs using various readability tests, sentiment analysis, accuracy and completeness scores provided by subject experts, and a PEMAT-P analysis.

Readability Tests

Based on the readability test results, it is evident that Gemini outperforms ChatGPT in terms of readability across all metrics. The Flesch reading ease score for Gemini is 52, indicating a moderately easy-to-read text, whereas ChatGPT scores lower at 39, suggesting a more challenging readability level. Similarly, Gemini demonstrates lower scores across the Gunning Fog index (11.8 compared to ChatGPT's 19.3), Flesch-Kincaid grade level (8.27 compared to ChatGPT's 14.86), and Smog index (8.21 compared to ChatGPT's 14.11). These results imply that Gemini's text is generally easier to comprehend and requires a lower reading grade level compared to ChatGPT's text (Table 1).

Table 1 Readability scores for both Chatbot generated PILs

Readability tests	ChatGPT	Gemini	
Flesch reading ease (FRE)	39	52	
Gunning fog index (GFI)	19.3	11.8	
Flesch-Kincaid grade level (FKGL)	14.86	8.27	
Smog index	14.11	8.21	

In the ChatGPT PIL, the automated readability index yielded a score of 16.95, indicating a difficulty level ranging from difficult to extremely difficult. The corresponding grade level was identified as college graduate, suitable for individuals aged 23 and above. In contrast, the Gemini PIL scored 8.14, indicating an average to slightly difficult reading difficulty. The grade level for Gemini was determined to be Eighth grade, suitable for individuals aged 13–14 years.

Sentiment Analysis

Based on the sentiment analysis scores, both ChatGPT and Gemini exhibit a positive tone. ChatGPT has a sentiment analysis score of 73.8%, while Gemini has a slightly higher score of 83.7%. These scores indicate a high level of positivity in the language used by both ChatGPT and Gemini, suggesting that their communications convey optimistic and favorable sentiments with considerable confidence (Table 2).

Table 2 Comparison of sentiment tone for ChatGPT vs Google Gemini

Sentiment analysis score	ChatGPT	Gemini	
Tone	Positive	Positive	
Confidence	73.8%	83.7%	

Accuracy and Completeness Scores

Five subject experts evaluated the accuracy and completeness of both PILs. The mean accuracy score for the ChatGPT PIL was 4.6 with a standard deviation of 0.49, while the mean accuracy score for the Gemini PIL was 4.2 with a standard deviation of 0.4. The mean completeness score for the ChatGPT PIL was 3.8 with a standard deviation of 0.75, whereas the mean completeness score for the Gemini PIL was 3.6 with a standard deviation of 0.49. These results suggest that both PILs were rated highly in terms of accuracy, but the ChatGPT PIL scored slightly higher in completeness compared to the Gemini PIL (Table 3 and Fig. 1).

Table 3 Mean ± SD scores for accuracy and completeness by the 5 experts

	Accuracy scores (Mean ± SD)	Completeness (Mean ± SD)	
ChatGPT	(4.6 ± 0.49)	(3.8 ± 0.75)	
Gemini	(4.2 ± 0.4)	(3.6 ± 0.49)	
p-value	0.195	0.631	

Fig. 1 Individual scores from each of the 5 experts (R1 to R5) Likert scale range 1–5, on accuracy and completeness of the two blinded PILs

PEMAT Analysis

A PEMAT analysis to assess the suitability of the PILs for their intended audience. The ChatGPT PIL scored 85% for understandability and 65% for actionability, while the Gemini PIL scored 90% for understandability and 90% for actionability. These results indicate that both PILs were highly understandable, but the Gemini PIL was rated higher in terms of actionability compared to the ChatGPT PIL (Table 4).

Table 4 The patient education materials assessment tool (PEMAT) analysis scores for ChatGPT and Google Gemini

PEMAT analysis	ChatGPT	Gemini	
Understandability	85	90	
Actionability	65	90	

Overall, the results of our study suggest that while both PILs provide valuable information on End-of-Life Care, there are differences in their readability, sentiment, accuracy, completeness, and suitability for their intended audience.

Discussion

Every individual facing a life-limiting illness deserves the right to experience a peaceful and dignified death. Achieving a ‘good death’ requires a comprehensive approach that considers the patients’ preferences, cultural backgrounds, ethical concerns, and effective symptom management. India's capacity to provide EOLC appears insufficient, as evidenced by its low ranking in global quality of death assessments. The lack of recognition of EOLC as a public health necessity, the absence of systematic data, and the absence of a national-level policy serve as significant barriers to EOLC implementation in developing nations. Legal uncertainties further hinder physicians from initiating EOLC discussions, leading to the unnecessary continuation of invasive life-sustaining treatments.7–9

The existing gaps in knowledge and misunderstandings, often stemming from limited personal experiences and reluctance to discuss taboo topics, might discourage individuals from seeking early access to comprehensive palliative care services as their illness progresses. These findings underscore the necessity for public education initiatives that surpass mere awareness-raising efforts and instead deliver essential messages through a public health lens. Such programs have the potential to shift attitudes towards palliative care, thereby enhancing end-of-life outcomes in the long run.10

Investigating the trajectory of EOLC discourse online in India has unveiled that discussions pertaining to EOLC are notably less prevalent compared to those centered on intensive care and palliative care, both within India and globally. This trend appears to be more pronounced in developed nations, where EOLC garners increased attention and search queries. Notably, among Indian states, Kerala emerges as the frontrunner in terms of the prominence of EOLC discussions (Fig. 2).11

Fig. 2 Trends/popularity of the topic “end-of-life care” followed over 12 months based on internet searches and mentions. Global and national trends (Google Trends)

Gursahani R, et al. have suggested involvement of commercial parties, general media campaigns, and policymakers can be possible avenues to raise public awareness in India about Normalizing death in day-to-day discourse in India.2

Tariq R, et al. highlighted the necessity for a standardized assessment framework for evaluating AI models in health care. This framework should not only gauge the accuracy and comprehensiveness of information but also guarantee that responses cater to the varied needs and backgrounds of patients. By incorporating feedback from a wide range of health care professionals and patients representing diverse backgrounds, AI models can be refined to prioritize patient-centered care. We call upon the medical community to collaborate in refining and establishing standards for AI interventions in healthcare.12

The findings of this study shed light on the multifaceted aspects of patient information leaflets (PILs) generated by AI platforms concerning EOLC. The study yielded several findings regarding the use of AI-generated patient information leaflets (PILs) in EOLC education. Firstly, both AI platforms, ChatGPT, and Google Gemini, demonstrated the capability to produce PILs with high levels of accuracy and completeness, as assessed by subject matter experts. This suggests that AI technologies can effectively distill complex healthcare information into understandable and comprehensive resources for individuals and families facing end-of-life issues. Furthermore, the sentiment analysis revealed that both ChatGPT and Google Gemini PILs conveyed predominantly positive tones, fostering hope, comfort, and trust among recipients. Additionally, Google Gemini's PIL exhibited superior readability and actionability compared to ChatGPT's PIL, indicating its potential to enhance accessibility and facilitate informed decision-making in diverse cultural contexts. Overall, these findings underscore the promising role of AI in advancing patient education and support in EOLC, potentially improving the quality of care and outcomes for individuals nearing the end of life.

Artificial intelligence chatbots utilize natural language processing algorithms to comprehend user queries or prompts, categorize them into predefined intents, maintain context, generate relevant responses, and present them in a human-readable format. Enhancing AI chatbots with more relevant prompts involves strategies like expanded training data, model fine-tuning based on user feedback, domain-specific customization, active learning, and human-in-the-loop systems. These approaches improve the chatbot's ability to interpret user intent accurately, provide contextually appropriate responses, and offer personalized assistance across various domains.13

However, while AI technologies hold promise in generating patient education materials, they come with inherent limitations and risks. One concern is the potential for AI hallucination, leading to the unintentional production of misleading or inaccurate information due to algorithm limitations or biases in training data.14 This poses ethical and safety implications, especially in sensitive healthcare contexts like EOLC. Additionally, relying solely on AI for content generation may diminish the role of human expertise and judgment, limiting the effectiveness and applicability of AI-generated texts in healthcare settings. Thus, while AI offers innovative opportunities in patient education, caution and awareness of its limitations are crucial to ensure the delivery of high-quality and ethically sound healthcare information.15

In 2017–2018, Supreme Court rulings in India reshaped EOLC, expanding constitutional rights to include privacy and autonomy. Notably, in the Common Cause vs The Union of India case, the right to execute advance directives (AD) and decline life-sustaining treatments was affirmed. Complex procedures prompted a simplification appeal led by the Indian Society of Critical Care Medicine (ISCCM). In 2023, the Supreme Court approved significant reforms. Advance directives no longer need validation by a Magistrate; notarization or attestation by a Gazetted officer suffices. The process for withdrawal or withholding decisions was streamlined to a two-tier system. Hospitals must now establish two medical boards, and the District Magistrate only needs to be informed, not authorized, of the decision. These changes aimed to simplify EOLC decision-making while maintaining a balance between legal clarity and individual autonomy.16,17

Implications for Patient Education and Healthcare Communication

The results of this study have significant implications for patient education and healthcare communication in the context of EOLC. By identifying areas of strengths and weaknesses in AI-generated PILs, healthcare providers, educators, and policymakers can refine existing patient education strategies and develop tailored interventions to meet the diverse informational needs of individuals and families facing end-of-life issues. Moreover, the insights gleaned from this study can inform the design and implementation of future AI-driven patient education initiatives, leveraging technology to enhance accessibility, effectiveness, and relevance in EOLC.

Limitations and Future Directions

We primarily relied on AI-generated Patient Information Leaflets (PILs), a technology undergoing rapid transformation. AI chatbot outputs can vary based on prompts, even generating different outputs with identical prompts. Future research could improve by considering multiple outputs to capture a broader spectrum of AI-generated content. Additionally, ChatGPT 3.5's limitations, including data timeframe constraints [before 2022], may affect the relevance of generated content. Furthermore, our subjective evaluations by experts may introduce subjective bias. The absence of real-world patient feedback limited our understanding of their comprehension of the educational material. Incorporating diverse perspectives, including those of patients, caregivers, and healthcare professionals, could enrich our understanding of patient education materials’ efficacy and impact on EOLC. Longitudinal studies are warranted to assess the sustained effects of AI-driven patient education initiatives on various aspects of EOLC, including patient outcomes, healthcare decision-making, and quality of life.

Conclusion

In conclusion, this study contributes to the burgeoning field of patient education in EOLC by offering insights into the comparative effectiveness of AI-generated PILs. While both ChatGPT and Google Gemini PILs provide valuable information on EOLC, differences in readability, sentiment, accuracy, completeness, and suitability underscore the need for ongoing refinement and innovation in patient education strategies. By leveraging the strengths of AI technologies and embracing a patient-centered approach, healthcare stakeholders can advance the delivery of compassionate, informed, and culturally sensitive care in EOLC, ultimately enhancing the well-being of individuals and families navigating the end-of-life journey.

Acknowledgments

The author would like to thank all the experts for their time and involvement.

Authors’ Contributions

PG, PK, PS: Substantial contributions to the conception or design of the work.

PG, SD, NG: The acquisition, analysis, or interpretation of data for the work.

PK, PS: Drafting the work or reviewing it critically for important intellectual content.

All: Final approval of the version to be published; AND all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Orcid

Prakash G Gondode https://orcid.org/0000-0003-1014-8407

Puneet Khanna https://orcid.org/0000-0002-9243-9963

Pradeep Sharma https://orcid.org/0009-0005-4875-0550

Sakshi Duggal https://orcid.org/0000-0002-8865-0854

Neha Garg https://orcid.org/0000-0003-4817-9807

Annexure A: Understanding End-of-life Care: A Guide for Everyone

Introduction: End-of-life care is a crucial aspect of healthcare that deserves attention and understanding from everyone, regardless of age, background, or health status. It's about ensuring dignity, comfort, and support for individuals nearing the end of their lives, as well as their families and loved ones. In this guide, we'll explore what end-of-life care entails and why it's important for all of us to be informed about it.

What is End-of-Life Care? End-of-life care refers to the support and medical care provided to individuals in the final stages of their lives. It focuses on enhancing quality of life, managing symptoms, and addressing the emotional, spiritual, and practical needs of patients and their families. End-of-life care can be provided in various settings, including hospitals, hospices, nursing homes, or even at home.

Key Components of End-of-Life Care:

Palliative Care: This type of care aims to relieve pain and other distressing symptoms, improve quality of life, and support patients and their families emotionally and spiritually.

Communication: Effective communication is essential in end-of-life care to ensure that patients, families, and healthcare providers understand each other's preferences, concerns, and goals.

Advance Care Planning: This involves discussing and documenting preferences for medical treatment and care in advance, should individuals become unable to communicate their wishes in the future.

Emotional and Spiritual Support: End-of-life care addresses the emotional and spiritual needs of patients and their families, providing counseling, bereavement support, and assistance in finding meaning and peace during this challenging time.

Practical Support: This includes assistance with activities of daily living, such as bathing, dressing, and eating, as well as practical matters like financial planning, legal issues, and funeral arrangements.

Why End-of-Life Care Matters to Everyone:

It's a Universal Experience: Death is a natural part of life, and at some point, everyone will be affected by it, whether personally or through the loss of a loved one.

Empathy and Compassion: Understanding end-of-life care fosters empathy and compassion towards those who are nearing the end of their lives and their families.

Preparedness: Being informed about end-of-life care empowers individuals to make informed decisions about their own care preferences and to communicate them to their loved ones and healthcare providers.

Dignity and Respect: End-of-life care aims to uphold the dignity and respect of individuals during their final days, ensuring that their wishes are honored and their comfort is prioritized.

Community Support: A well-informed community that understands and values end-of-life care can advocate for improved access to quality services and support for individuals and families facing end-of-life issues.

Conclusion: End-of-life care is not just about medical interventions; it's about compassion, dignity, and respect for human life. By educating ourselves about end-of-life care and engaging in open conversations about death and dying, we can contribute to a more compassionate and supportive society for everyone, regardless of their stage in life. Let's embrace the importance of end-of-life care and work together to ensure that everyone receives the care and support they deserve at the end of life.

Annexure B: Empowering Your Journey: A Guide to End-of-life Care

Introduction

Talking about end-of-life care can be difficult, but it's a crucial conversation for everyone. This guide, brought to you by (PIL Name), is designed to empower you with knowledge and resources about your options. (light bulb emoji)

What is End-of-Life Care?

End-of-life care focuses on providing comfort and support to individuals facing a serious illness and their loved ones. It's not just about medical treatment; it's about honoring your wishes and ensuring a peaceful transition. (dove emoji)

Key Components:

Pain & Symptom Management: This aims to relieve pain and discomfort, allowing you to focus on quality time with loved ones. (adhesive bandage emoji).

Emotional & Spiritual Support: Chaplains, social workers, and therapists can provide emotional and spiritual support for you and your family. (people hugging emoji) (folded hands emoji).

Advance Directives: These documents allow you to express your wishes for medical care in advance, ensuring your voice is heard. (page facing up emoji) (megaphone emoji).

Palliative Care: This specialized care focuses on symptom relief and improving quality of life for those with serious illnesses. (mended heart emoji).

Hospice Care: Hospice provides comfort care and support for those nearing the end of life, typically at home or in a hospice facility. (house emoji) (hospital emoji).

Why is Planning Important?

Planning for end-of-life care allows you to:

Make informed choices: Knowing your options empowers you to make decisions that align with your values. [check mark emoji].

Reduce stress: Open communication with loved ones about your wishes can lessen burdens later. [relieved face emoji].

Ensure your wishes are respected: Advance directives ensure your voice is heard regarding your care. [ear emoji].

Provide comfort for loved ones: Knowing your wishes can bring peace of mind to your family. [dove emoji].

How to Get Started:

Talk to your doctor: Discuss your concerns and explore end-of-life care options. [man doctor emoji] [woman doctor emoji].

Research: Learn about different types of care and available resources. [magnifying glass emoji].

Complete advance directives: Appoint a healthcare proxy and document your wishes for medical treatment. [pen and paper emoji] [handshake emoji].

Talk to loved ones: Open communication is key. Share your wishes and listen to theirs. [megaphone emoji] [ear emoji].

Additional Resources:

National Hospice and Palliative Care Organization: https://www.nhpco.org/.

https://www.medicare.gov/coverage/hospice-care.

National Center on Advance Care Planning: [invalid URL removed].

Remember: End-of-life care is about honoring your wishes and ensuring a peaceful journey. By planning ahead and having open conversations, you empower yourself and your loved ones.

PIL Name is here to support you on this journey. We offer resources and information to help you make informed decisions.

Please note: This guide provides general information. Always consult your doctor for personalized advice.

Source of support: Nil

Conflict of interest: None
==== Refs
References

1. Myatra SN Salins N Iyer S Macaden SC Divatia JV Muckaden M et al. End-of-life care policy: An integrated care plan for the dying: A joint position statement of the Indian Society of Critical Care Medicine (ISCCM) and the Indian Association of Palliative Care (IAPC) Indian J Crit Care Med 2014 9 615 635 10.4103/0972-5229.140155.2
2. Gursahani R Mani RK Simha S Making end-of-life and palliative care viable in India: A blueprint for action Natl Med J India 2019 3 129 133 10.4103/0970-258X.278684
3. Pallium India Quality of Death Index 2021. Pallium India. Available from: https://palliumindia.org/2022/01/quality-of-death-index-2021#:~:text=India%20ranked%2067th%20among%2080,59th%20among%2081%20countries%20studied
4. Gursahani R Mani RK India: Not a country to die in Indian J Med Ethics 2016 1 1 30 35 10.20529/IJME.2016.007 26826659
5. Bhattad PB Pacifico L Empowering patients: Promoting patient education and health literacy Cureus 2022 14 7 e27336 10.7759/cureus.27336 36043002
6. Cheong RCT Unadkat S Mcneillis V Williamson A Joseph J Randhawa P et al. Artificial intelligence chatbots as sources of patient education material for obstructive sleep apnoea: ChatGPT versus Google Bard Eur Arch Otorhinolaryngol 2024 281 2 985 993 10.1007/s00405-023-08319-9 37917165
7. Divatia JV Amin PR Ramakrishnan N Kapadia FN Todi S Sahu S et al. INDICAPS study investigators. Intensive care in India: The Indian intensive care case mix and practice patterns study Indian J Crit Care Med 2016 20 4 216 225 10.4103/0972-5229.180042 27186054
8. Bhatnagar S Biswas S Kumar A Gupta R Sarma R Yadav HP et al. Institutional end-of-life care policy for inpatients at a Tertiary Care Centre in India: A way forward to provide a system for a dignified death Indian J Med Res 2022 155 2 232 242 10.4103/ijmr.IJMR_902_21 35946200
9. Menon MR Ethics and medicolegal aspects of withdrawal of treatment in critical care patients without advanced directives in India: Who will guard the guardians themselves? Indian J Crit Care Med 2023 28 1 15 17 10.5005/jp-journals-10071-24600
10. McIlfatrick S Slater P Beck E Bamidele O McCloskey S Carr K et al. Examining public knowledge, attitudes and perceptions towards palliative care: A mixed method sequential study BMC Palliat Care 2021 20 1 44 10.1186/s12904-021-00730-5 33731087
11. Google Trends Google Trends - Explore. Google Trends; Available from: https://trends.google.com/trends/explore?q=%2Fm%2F02qyw_s&date=now%201-d&geo=IN&hl=en
12. Tariq R Khanna S Artificial intelligence-powered patient education for comprehensive and individualized understanding for patients Clin Gastroenterol Hepatol 2023 S1542–3565(23)00910-2 10.1016/j.cgh.2023.10.027
13. Dwivedi YK Kshetri N Hughes L Slade EL Jeyaraj A Kar AK et al. “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy Int J Inform manage 2023 71 102642 10.1016/j.ijinfomgt.2023.102642
14. Hatem R Simmons B Thornton JE A call to address AI “Hallucinations” and how healthcare professionals can mitigate their risks Cureus 2023 15 9 e44720 10.7759/cureus.44720 37809168
15. Al Kuwaiti A Nazer K Al-Reedy A Al-Shehri S Al-Muhanna A Subbarayalu AV et al. A review of the role of artificial intelligence in healthcare J Pers Med 2023 13 6 951 10.3390/jpm13060951 37373940
16. Mani RK Simha S Gursahani R Simplified legal procedure for end-of-life decisions in India: A new dawn in the care of the dying? Indian J Crit Care Med 2023 27 5 374 376 10.5005/jp-journals-10071-24464 37214121
17. Indian Association of Palliative care IAPC news announcements laws. supreme court judgement copy: Advance medical directives (Living Will) and withdrawal of life support treatment. Available at: https://www.palliativecare.in/supreme-court-judgement-copy-advance-medical-directives-living-will-and-withdrawal-of-life-support-treatment/
