ShareEmailPrint To learn more, read: Posted on May 5, 2016May 4, 2018By: Kayla McGowan, Project Coordinator, Women and Health Initiative, Harvard T.H. Chan School of Public HealthClick to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on LinkedIn (Opens in new window)Click to share on Reddit (Opens in new window)Click to email this to a friend (Opens in new window)Click to print (Opens in new window)In honor of International Day of the Midwife, I sat down with Rima Jolivet, our Maternal Health Technical Director, for insight into her experience as a certified nurse-midwife as well as her thoughts on the current and future landscape of midwifery.How did you first become interested in the field of maternal health and midwifery?RJ: I got interested in midwifery because my son was born in the U.K., in the National Health Service system, where midwifery is standard of care for low-risk women. I arrived there about seven and a half months pregnant and was funneled into midwifery care because I was young and low-risk. And I thought it was amazing. I thought the system was organized in a very different way from our [U.S.] system. It seemed to me that it was organized around the needs and preferences of women and families for the most part. Although it wasn’t fancy, it was very much oriented toward being people-centered. I had a great experience and it made me want to make that kind of care available to more women.What is it like being a midwife? Could you tell me a bit about the challenges as well as some of the rewarding aspects of the career?RJ: What I did not expect about being a midwife is that in the U.S., midwifery is not only a profession, but also sort of a political struggle, because the midwifery model of care is not the standard of care. Part of being a midwife in this country is being a political activist for the midwifery model of care, which I didn’t expect. The midwifery model of care is evidence-based, centered on respectful care, and yet, there’s a lot of struggle to implement it because of structural and ideological differences. There are certain interventions or choices – for example, vaginal birth after Cesarean section or vaginal breech delivery when the circumstances are appropriate, or delivery outside of the hospital – that are very much supported in other systems, and all cadres work together to make those as safe and effective as possible. That’s not the case here. There’s a lot of battling about whose evidence is better, and who’s right, as opposed to working together to make choices that women want available, safe, and effective. Midwives attend about 12% of all vaginal births and 8% of total births in the U.S., so it’s not the mainstream approach, although it’s very much supported by evidence.What are some of the barriers midwives face in providing quality care? RJ: There has been a series of midwifery strategy meetings going on recently, co-led by WHO, the International Confederation of Midwives (ICM), White Ribbon Alliance, and USAID, and the WHO presented a body of research on barriers and challenges for midwives to practice quality midwifery care. The research describes challenges in three domains: economic, professional, and social. It lays out patterns of economic discrimination that mirror gender inequality in the workplace; the professional area has to do with lack of professional legitimacy, authority and respect; and the social domain has to do with how midwives are perceived, their social status, as compared to other providers.RJ: One challenging issue is that there are many different definitions of midwives, and even the idea that other types of health workers can provide “midwifery care.” The ICM has been promoting a standard definition of the midwife. Professional groups in countries are working to meet this definition. For example, in the U.S., there’s been a big push by direct entry (or “lay”) midwives, who are not nurse midwives, to meet ICM and other criteria for a qualified maternal health provider and to obtain legal licensure in all the jurisdictions of this country. In the last decade, there’s been a lot of progress for certified professional midwives. There have also been great strides in the attempts to bring all of the kinds of midwives together under one big tent, to work together in coalition.What would you say is the biggest misconception regarding the field of midwifery, either here in the U.S. on in low-income settings?RJ: In the U.S., there are a lot of misconceptions about midwives and their scope of practice, for example the idea that midwives only attend out-of-hospital births or that you can’t have an epidural with a midwife or that midwives aren’t well trained. A lot of people don’t know that midwives in the U.S. provide the full scope of women’s health care, including contraception and family planning, and routine gynecology. Similar misconceptions may also exist in low- and middle-income countries where midwives are sometimes perceived as unqualified.According to the Lancet series on midwifery, universal coverage of essential interventions that fall within the scope of midwifery practice (including pre-pregnancy, antenatal, labor, birth, and postpartum care and family planning) could prevent 83% of all maternal deaths, stillbirths, and neonatal deaths. This is monumental! Could you explain this a bit? What are some of the most significant ways midwives can save the lives of moms and babies? RJ: Nearly all essential life-saving interventions are within the scope of practice of midwives, and if there were enough midwives to ensure universal coverage of those interventions, that’s how the lives would be saved. It’s a high value, relatively low-cost investment in midwifery. It’s a cost-effective solution if it were implemented and scaled up.Could you tell me about the role of midwives in achieving the Sustainable Development Goals (SDGs) and meeting the strategies toward ending preventable maternal mortality (EPMM) targets?RJ: EPMM takes a very broad approach to maternal health and survival, which includes a focus on health system strengthening to ensure equitable access to high quality care for every woman and baby. As we just discussed, achieving this means addressing the workforce issues in every setting to be sure there are enough qualified midwives, themselves a high-quality, cost-effective solution, working in functional, well-equipped care settings. Effectively scaling up midwifery care will help achieve the SDG/EPMM targets. Midwives have the capacity and the potential to really contribute to mortality reduction. But they need the support of the enabling environment to do that: more midwives need to be trained so there are sufficient numbers, and attention is needed to ensure the availability of sufficient, functional facilities, with good referral systems and essential commodities. In lots of low- and middle-income countries, midwives are the tip of the spear, and all of the burden is on them without the enabling environments they need to ensure quality of care. The studies commissioned by WHO highlight the gender dimension of midwifery. Midwives and nurses tend to be women, and as frontline workers they do not receive as much respect as physicians, so their status within health systems is perceived as lower and they are poorly represented in leadership and decision-making positions. They bear an inordinate burden of deficient health systems without the agency to effect change. They are at the front line without the necessary materials and protections: commodities, infrastructure, support, and respect.According to the UNFPA, of the 73 countries that make up 92% of all maternal and newborn deaths in the world, only 4 have a midwifery workforce sufficient to meet the universal needs for reproductive, sexual, maternal, and newborn health. How do you think the global health community can scale up midwifery programs to ensure adequate human resources and quality care?RJ: There are issues of education and training of midwives, as well as their distribution in the workforce (recruiting, retention, and getting midwives where they’re most needed in sufficient numbers). But if the working conditions are as difficult as they are in many high burden countries, how do you sell that? It can be a hard sell.The ICM has a new resource called the Midwifery Services Framework (MSF), which helps countries interested in strengthening midwifery go through a process to look at their need, starting with the question, “What is the essential service package that all women should get?” From there, the framework walks decision makers through a process to identify how many of those interventions could be delivered by midwives, how many midwives would be needed to provide coverage of those interventions to all of the women in the country, and then what that implies in terms of need to scale up education programs and training for midwives. The MSF walks stakeholders through that entire workforce development cascade based on a rational decision-making process to analyze the need.The theme of IDM 2016 is “Women and Newborns: The Heart of Midwifery,” and the International Confederation of Midwives is asking midwives to share their stories on social media. How would you complete this sentence, “I am a midwife, this is what I do…”?RJ: For me, this goes back to the reason I became a midwife. What midwives do (and as a midwife, this is what I try to do) is to put women and families at the center of all we do…including health care planning, implementation, and continuous improvement to better reflect the needs, values, and preferences of the people the health system is for.Where do you see the future of midwifery?RJ: I see midwifery in the future respected and acknowledged as the global standard of care, with all of the support and the enabling factors needed to bear that standard in the areas of education and training, workforce protections, and adequate infrastructure, commodities, team-based professional support and functional referral systems.—To learn more about the state of midwifery around the globe, read our roundup: #IDM2016: Key Resources for Midwifery!Join us in celebrating International Day of the Midwife! Follow along on Twitter by using #IDM2016Share this:
Just as Google offers one place to search for all online data, Alation is hoping to offer a single place for enterprises to search their data. Alation, launched officially this morning, is a startup with software that ties into a company’s data stores, and then gives users a natural language search interface for querying that information.Satyen Sangani, CEO and cofounder of Alation, said the company has been working on this technology for two years. “We partnered with massive companies that have lots of data; our installation gets us up and running in days or hours, in some cases,” he said.“We’re able to parse through query logs from 20-odd database systems, and all that happens pretty seamlessly. You point us to the underlying data storage, either through URI or some underlying data connection.” Making natural language queries available on enterprise data sources allows everyone to have access to the data, not just scientists, said Sangani. “A query could look like, ‘Tell me how many doctors there are in Nebraska.’ It does translate natural language search, giving you the context of the data. We tell you not just the table, but also that Jim has used this table 400 times in the last month. We give you a whole bunch of context that’s not a part of the data itself,” he said.Alation is also a collaborative platform, said Sangani. “There’s a collaborative layer. We give you a StackOverflow-like interface on top of the data, so people can start talking about how they handle the data. Now that you can find the data, you can find what people have done with it,” he said.Sangani said that Alation’s approach is the opposite of traditional data storage thinking. “The historic approach has been, ‘Let’s go ahead and document the data through some massive human-based effort,’ ” he said. “That’s like painting the Golden Gate Bridge: By the time you get to one end, you have to start over and repaint it again.“We think of this kind of as a search problem. Every time someone creates a data model, they’re creating a new bit of information. The minimum viable product for search is an extremely high bar. People have tried to document data for many decades. We’re aware of the many failures before us.”
Knowledge is the foundation of intelligence— whether artificial intelligence or conventional human intellect. The understanding implicit in intelligence, its application towards business problems or personal ones, requires knowledge of these problems (and potential solutions) to effectively overcome them.The knowledge underpinning AI has traditionally come from two distinct methods: statistical reasoning, or machine learning, and symbolic reasoning based on rules and logic. The former approach learns by correlating inputs with outputs for increasingly progressive pattern identification; the latter approach uses expert, human-crafted rules to apply to particular real-world domains.RELATED CONTENT: Ethical design — What is it and why developers should careTrue or practical AI relies on both approaches. They supplement one another for increasingly higher intelligence and performance levels. Enterprise knowledge graphs— domain knowledge repositories containing ideal machine learning training data—furnish the knowledge base for maximum productivity of total AI. Symbolic reasoningKnowledge graphs are at the basis of symbolic reasoning systems using expert rules for real-life business problems. Regardless of the particular domain, data source, data format, or use case, they seamlessly align data of any variation according to uniform standards focused on relationships between nodes. Semantic rules and inferencing create new types of understanding about business knowledge that machine learning couldn’t generate at all. Examples include optimizing the array of sensor data found in smart cities for event planning based on factors such as traffic patterns, weather conditions, previous event outcomes, and preferences of the hosts and their constituencies. Symbolic reasoning also has the advantage that in the end, one still can explain how and why certain new knowledge and new suggestions were generated.Statistical feedback loopDespite what has been said in the previous paragraphs, knowledge graphs can greatly benefit from machine learning and add value to the symbolic rules-based systems. When modeling car driving behavior, for example, modern image recognition systems (relying on deep learning) can produce more realistic models when deployed in conjunction with rules. Nonetheless, the general paradigm by which machine learning complements rules-based AI is by creating a feedback mechanism for improving the latter’s outcomes—and enhancing the knowledge of semantic graphs.In the preceding smart city use case organizations can deploy machine learning to the outcome of rules-based systems, especially when those outcomes are measured in terms of KPIs. These metrics can assess, for example, the success of the event as measured by the enjoyment of the attendees, the subjective costs and the real costs to the municipality, these costs for the organizations involved in the event, the rate of attendance, etc. Machine learning algorithms can analyze those KPIs for predictions to improve future events.Horizontal applicabilityThe interplay of the knowledge graph foundation with both the statistical and symbolic reasoning form of AI is critical for several reasons. Firstly, they all augment each other. The graphs provide the knowledge for rules-based systems and optimize machine learning training data. The machine learning feedback mechanism improves the graph’s knowledge and the rules, while the output of rules-based systems provides knowledge upon which to run machine learning. Secondly, this process is applicable to any number of horizontal use cases across industries. Most of all, however, there are amazingly advanced applications of AI empowered by this combination, the likes of which makes simple automation seem mundane.There’s risk management use cases in law enforcement and national security in which one can observe terrorists, for example, integrate that information and create hypothetical events or scenarios based on probability (determined by machine learning). Rules-based systems for security measures, then, are transformed into probabilistic rules-based systems that unveil the likelihood of events occurring and how best to mitigate them. Similar processes apply to many other instances of risk management.