Month: April 2023

Promoting Respectful Maternal Care: Ethical, evidence-based and dignified care during facility-based births

Authors: Dr. Srilekha C., Medical Consultant

The Shift in Maternal Care

Over a few decades, women are constantly encouraged to give birth in health care facilities so they may have access to skilled health care professionals and should the need for additional care arise the measures can be taken. However, accessing skilled and professional care in health facilities may not completely guarantee respectful, good, and quality care. Every woman has the right to have a positive and pleasant childbirth experience with Quality care. Ideally the expected quality care includes respectful care of the woman in labour, good and respectful communication, and making sure she feels emotionally and socially supported in the way that she desires.

Worldwide, maternal health eorts are shifting from an emphasis on boosting service utilization to improving quality of care. This change has been accompanied by a growing body of work on how women are treated during facility-based childbirth, which was rst brought to global attention in 2010 by Bowser and Hill’s landscape analysis. This chilling quote from the report highlights how women may experience physical abuse during labour.

“When a woman goes into the second stage of delivery, you don’t want her to close her legs, so you’re beating her” [Kenya] (Center for Reproductive Rights & Federation of Women Lawyers–Kenya (FIDA), 2007)

Respectful maternity care (RMC) provides meaningful experiences of childbirth as a basic element of quality health, which includes knowing their self-worth, feelings, and preferences. RMC is globally recognized. Every woman around the world has a right to receive respectful maternity care. The concept of “respectful maternity care” has evolved and expanded over the past few decades to include diverse perspectives and frameworks. Advocates emphasized the need to humanize birth, taking a more holistic approach.

What it takes

Respectful maternity care (RMC) is not only a crucial component of quality of care; it is a human right. In 2014, WHO released a statement calling for the prevention and elimination of disrespect and abuse during childbirth, stating that “every woman has the right to the highest attainable standard of health, including the right to dignied, respectful care during pregnancy and childbirth.”WHO also called for the mobilization of governments, programmers, researchers, advocates, and communities to support RMC. In 2016, WHO published new guidelines for improving quality of care for mothers and new-borns in healthcare facilities, which included an increased focus on respect and preservation of dignity. In addition to the eight domains of quality of care, the framework also includes six strategic areas to help build a systematic, evidence-based approach for providing quality care: Clinical guidelines, Standards of care, Eective interventions, Quality measures, and Relevant research and capability building.

The ‘Holistic Way’

RMC is an approach centered on the individual, based on principles of ethics and respect for human rights, and promotes practices that recognize women’s preferences and women’s and new-borns’ needs.
Understanding a woman’s perspective and her needs during childbirth and addressing them as part of quality-improvement programmes can make delivery care safe, aordable, and respectful. The patient’s judgement on the quality and goodness of care is indispensable to improving the management of healthcare systems.
Respectful Maternal Care (RMC) is dened as “care organized for, and provided to all women in a manner that maintains their dignity, privacy and condentiality, ensures freedom from harm and mistreatment and enables informed choice and continuous support during labor and childbirth.”

The Real Plight

It is a human right for every woman to deserve respectful maternity care, yet many women abstain from obtaining professional maternity care due to the lack of respect and being abused during labour. As per research and reports this leads to birth injury, and maternal and new-born deaths. Despite such severe impacts, these issues remain under wraps and especially so in developing nations.
Disrespectful and undignied care is prevalent in many facility settings in India, particularly for underprivileged populations, and this not only violates their human rights but is also a signicant barrier to accessing intrapartum care services.
Bowser and Hill categorized disrespect and Abuse (D&A) faced by pregnant women into seven major categories – physical abuse, they are non-consented clinical care, non-condential care, non-dignied care (including verbal abuse), discrimination based on specic patient attributes, abandonment, denial of care, and detention in facilities.
Improving the quality of healthcare services by providing respectful maternity care (RMC) could lead to further reduction in Maternal mortality rate (MMR). Adopting a patientcentric approach and training of providers focused on respectful care would signicantly improve the quality of care, promote institutional births, and protect the fundamental right of women to equity, dignity, and respect.

A Pathway Towards Respectful Maternity Care…

Several studies have documented the ubiquitous nature of disrespectful care and its adverse eects on care-seeking behaviour, and calls to action on quality of maternal health care have prioritised women’s experiences.

The ndings from a study with an objective to estimate the prevalence of Disrespect and Abuse (D&A) and its determinants during pregnancy, childbirth, and immediate postpartum period among women in India provide crucial information to widen the scope of research on the eects of socioeconomic status on Respectful Maternal Care.

A study was conducted at Jamtara district in Jharkhand, with an objective to understand the aspects of care that women consider important during childbirth6\. Jharkhand is a state in eastern India with poor maternal and child health indicators.

Aspects of care most cited by women to be important in facility-based childbirth were – the availability of health providers and appropriate medical care (primarily drugs) in case of complications, emotional support, privacy, clean place after delivery, availability of transport to reach the institution, monetary incentives that exceed expenses, and prompt care. While some other factors include kind interpersonal behaviour, cognitive support, faith in the provider’s competence, and overall cleanliness of the facility and delivery room.

A low cost RMC-promoting interventions recommended by the WHO is the presence of a birth companion during labour and delivery. A birth companion is not only a key component to full the objective of RMC, but, is also vital in improving the quality of care during labour and delivery.

Presence of a Birth companion during labour in a labour ward usually was not a regular practice in the past, hence there was an unmet need to implement this initiative to improve the quality of maternity care and to ensure a positive pregnancy outcome for every woman. Despite the known benets, allowing birth companions is not in practice in government set-ups and in most private healthcare facilities in India. There is also a dearth of literature focusing on the method of implementation of a birth companion, which is a major challenge in busy labour and delivery wards.

A study was conducted in the Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences, New Delhi with the objective to establish a practice of allowing a companion during labour and delivery. It followed the principles of Quality Improvement (QI), which included analysis of the problem and implementation of Plan-Do-Study-Act (PDSA) cycles for a step-by-step approach to provide quality care. A QI team was formed, and after obtaining the baseline data, problems were analysed using sh bone chart. A new policy of allowing birth companion was made and eorts made to sensitise and train the doctors and nurses posted in labour ward.

Simple interventions such as dress code for birth companions, curtains for ensuring privacy, display of posters and frequent reminders on WhatsApp groups were planned. The results of the study were the median value of women accompanied by birth companion marginally increased to 25% after the rst PDSA cycle. Implementation of further changed ideas led to increase in median, which reached 66.6%. Thereafter, there was a decline, but by the end of 6 months, it was possible to attain the goal to establish a practice of allowing a companion during labour and delivery by using the principles of quality improvement (QI)and sustain it.

The findings revealed a direct relationship between respectful maternity care and positive childbirth experience7. Therefore, it is recommended that mangers and policy makers in childbirth facilities reinforce facilitating respectful maternity care to improve women’s child birth experience and prevent potential adverse eects of negative childbirth experiences.

A simple tool that standardizes and measures whether best practices in RMC are being followed in the labour ward is the new WHO Labour Care Guide. The Labour Care Guide replaces the WHO partograph. It places an importance on not just adherence to process, but also the quality of the service delivery and person-centered care.

Intelehealth along with several clinical partners is working on a digital health solution, called eZazi, that adapts the WHO Labour Care Guide to a digital tool with integrated telemedicine support to improve the quality of care delivered in labour wards. This new product is another step towards achieving our vision of ethical, evidence-based and dignied healthcare for every woman.

The Need of the Hour

Raising awareness and strengthening the capacity of the interested parties regarding concepts and practices related to respectful maternity and new-born care; identifying the legal frameworks which support it; recognizing and describing the use of some of these tools to plan, implement and monitor quality improvement initiatives for maternity and new-born care in health care services.

The need of the hour is collaboration and investment that include diverse donors, partners, and experts with an interest in RMC for a sizeable impact.

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The Future of Digital Healthcare: How AI and ML are transforming Telemedicine and impacting patient outcomes

Authors: Ramesh C, CTO

A Force to be reckoned with!

Articial intelligence (AI) is a high-powered and inuential area of computer science, with the ability to radically rebuild the practice of medicine and an enormously impactful delivery of healthcare.

What is AI? Simply put, AI refers to the science and engineering of making intelligent machines, through algorithms or a set of rules, which the machine follows to mimic human cognitive functions, such as learning and problem solving. AI systems have the potential to anticipate problems or deal with issues as they come up and, as such, operate in an intentional, intelligent and adaptive manner. AI’s strength is in its ability to learn and recognise patterns and relationships from large multidimensional and multimodal datasets; for example, AI systems could translate a patient’s entire medical record into a single number that represents a likely diagnosis. Moreover, AI systems are dynamic and autonomous, learning and adapting as more data become available.

In recent years, we have witnessed the integration of Articial intelligence (AI) and Machine learning (ML) in various industries and healthcare is no exception. Digital healthcare oers a huge range of possibilities and may improve the quality of patient care. The traditional paradigm of clinical history, examination, dierential diagnosis, and treatment may be improved by tools such as machine learning, mobile applications and sensors, wearables, and telehealth. The recent pandemic has accelerated the move towards this future, COVID-19 has only enhanced this impact and one of the areas that has seen signicant transformation

is healthcare, especially with telemedicine emerging as a critical tool for providing remote healthcare services to patients in the last mile populations.


The Paradigm Shift

The application of technology and artificial intelligence (AI) in healthcare has the potential to address
supply-and-demand challenges. The increasing availability of multi-modal data (genomics, economic,
demographic, clinical and phenotypic) coupled with technology innovations in mobile, internet of things
(IoT), computing power and data security herald a moment of convergence between healthcare and technology to fundamentally transform models of healthcare delivery through AI-augmented
healthcare systems.
AI-ML is changing the landscape of healthcare by enhancing digital health and telemedicine services. In
this blog post, we will explore how AI-ML is transforming healthcare, specifically through NLP &
chatbots, AI-assisted disease screening, differential diagnosis, clinical decision support systems and
analytics of Electronic Medical Records (EMRs).

NLP and Chatbots

Natural language processing (NLP) is a subfield of AI that enables computers to understand, interpret, and generate human language. NLP based chatbots have significant potential in healthcare, as they can be used to improve communication between patients and healthcare providers. Chatbots can provide patients with accurate and relevant

information, helping them make informed decisions about their health. This especially plays a key role in the management of lifestyle diseases such as Obesity, diabetes, hypertension & heart disease. Imagine a patient seeking advice on diet & lifestyle

mgmt. being greeted by a friendly chatbot that guides based on clinically validated protocols without a health provider’s intervention. Additionally, chatbots can triage patients, helping to reduce wait times and reduce the burden on healthcare professionals. With significant advancements in NLP especially with advanced language models such as ChatGPT, GPT-3 from OpenAI and LaMDA from Google; the world of NLP is here to stay and Telemedicine only stands to gain immensely from this.

AI-assisted disease screening

AI-ML can assist in screening for various diseases such as cataract, anaemia, malnutrition, maternal health care and infant mortality. For example, AI prediction models can analyse eye images to identify early signs of cataract as well as help screen Anaemia by looking closer at the palpebral conjunctiva. In underserved communities, where access to healthcare is very limited, this automated disease screening

can be a boon and help prevent severe disease progression and even deaths. Similarly, ML can identify patterns in malnutrition and maternal healthcare, making timely predictions of childbirth outcomes and related complications. This helps to improve the quality of care and reduce maternal & infant mortality rates. An AI-ML based screening model used in Telemedicine amplifies the impact, in terms of early detection of disease & preventive healthcare especially in hard-to-reach remote communities.

Differential diagnosis

Differential diagnosis is the process of identifying a patient’s medical condition by comparing their symptoms with those of other possible conditions. This process can be challenging, as many diseases have similar symptoms. AI-ML can help with this process by analysing patient data and identifying patterns that are associated with specific diseases. This can help healthcare professionals arrive at an accurate diagnosis more quickly, leading to faster treatment and better outcomes for patients. This can also serve as a “effective eye for blind spots” in medical diagnosis and help physicians arrive at a more accurate diagnosis.

Clinical decision support systems

Clinical decision support systems (CDSS) are computer based tools that provide healthcare professionals with information and knowledge to assist with decision-making in patient care. AI-ML can enhance the capabilities of CDSS by providing real-time analytics and personalized recommendations based on patient data. For example, AI algorithms can analyse a patient’s EMR and recommend specific treatments based on the patient’s medical history, symptoms, and other factors.

Analytics of EMRs

EMRs are digital records of patients’ medical histories, treatments, and outcomes. AI-ML can analyse these records to identify patterns and trends that can help healthcare providers make more informed decisions. For example, analytics of EMRs can help identify disease seasonality patterns and predict outbreaks, enabling healthcare providers to prepare for potential public health crises. Additionally, analytics of EMRs can help

identify high-risk patients/societies who may benefit from preventative interventions. This helps deliver focused & timely interventions that lead to better health outcomes & lower healthcare costs.

Building effective and trusted digital healthcare systems

In conclusion, AI-ML is transforming healthcare by enhancing digital health

and telemedicine services. However, there are also challenges associated with AI-ML when dealing with huge volumes of patient data. Patient data privacy & confidentiality become key aspects to be considered when building software systems. There are several laws & regulations such as HIPAA that

help moderate this and protect patient interest, while still enabling delivery of quality healthcare. As AI- ML continues to evolve, we can expect significant improvements in healthcare delivery and outcomes.

AI today, and in the near future…

At Intelehealth, we constantly leverage advances in technology to build better digital health solutions that are focused on serving the underserved communities. With this we help to bridge the inequities in healthcare and strive to produce better patient outcomes!

Digital health: how it started, how it is going and how it could be. Three panels presenting traditional care (‘How it started’), current care (‘How it is going’) and a possible future care (‘How it could be’) paradigm.

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