Artificial intelligence for global health

Artificial intelligence (AI) has demonstrated great progress in the detection, diagnosis, and treatment of diseases. Deep learning, a subset of machine learning based on artificial neural networks, has enabled applications with performance levels approaching those of trained professionals in tasks including the interpretation of medical images and discovery of drug compounds (1). Not surprisingly, most AI developments in health care cater to the needs of high-income countries (HICs), where the majority of research is conducted. Conversely, little is discussed about what AI can bring to medical practice in low- and middle-income countries (LMICs), where workforce shortages and limited resources constrain the access to and quality of care. AI could play an important role in addressing global health care inequities at the individual patient, health system, and population levels. However, challenges in developing and implementing AI applications must be addressed ahead of widespread adoption and measurable impact.

Health conditions in LMICs and HICs are rapidly converging, as indicated by the recent shift of the global disease burden from infectious diseases to chronic noncommunicable diseases (NCDs, including cancer, cardiovascular disease, and diabetes) (2). Both contexts also face similar challenges, such as physician burnout due to work-related stress (3), inefficiencies in clinical workflows, inaccuracies in diagnostic tests, and increases in hospital-acquired infections. Despite these similarities, more basic needs remain unmet in LMICs, including health care workforce shortages, particularly specialist medical professionals such as surgical oncologists and cardiac care nurses. Patients often face limited access to drugs, diagnostic imaging hardware (ultrasound, x-ray), and surgical infrastructures (operating theaters, devices, anesthesia). When equipment is available, LMICs often lack the technical expertise needed to operate, maintain, and repair it. As a result, 40% of medical equipment in LMICs is out of service (4). Conditions are exacerbated in fields that require both specialized workforce and equipment. For example, delivering radiotherapy requires a team of radiation oncologists, medical physicists, dosimetrists, and radiation therapists—together with sophisticated particle accelerator equipment. Consequently, 50 to 90% of cancer patients requiring radiotherapy in LMICs lack access to this relatively affordable and effective treatment modality (5).

LMICs have undertaken substantial health care spending, saving millions of lives by improving access to clean water, vaccinations, and HIV treatments. However, changes in health care needs owing to increased mortality from complex NCDs require high-quality, longitudinal, and integrated care (6). These emerging challenges have been central to the United Nations’ Sustainable Development Goals, including the aim to reduce by one-third premature mortality from NCDs by 2030. AI has the potential to fuel and sustain efforts toward these ambitious goals.

Health care–related AI interventions in LMICs can be broadly divided into three application areas (see the figure). The first includes AI-powered low-cost tools running on smartphones or portable instruments. These mainly address common diseases and are operated by nonspecialist community health workers (CHWs) in off-site locations, including local centers and households. CHWs may use AI recommendations to triage patients and identify those requiring close follow-up. Applications include diagnosing skin cancer from photographic images and analyzing peripheral blood samples to diagnose malaria (7); more are expected given the emergence of pocket diagnostic hardware, including ultrasound probes and microscopes. With increasing smartphone penetration, patient-facing AI applications may guide lifestyle and nutrition, allow symptom self-assessment, and provide advice during pregnancy or recovery periods—ultimately allowing patients to take control of their health and reducing the burden on limited health systems.

The second application area focuses on more specialized medical needs, with the goal of supporting clinical decision-making. AI may allow nonspecialized primary care physicians to perform specialized tasks including reading diagnostic radiology and pathology images, only referring to specialists if necessary. AI tools may also help provide specialists with expert knowledge across multiple subspecialties. This is particularly important in oncology, where lack of subspecialists may force an oncologist to manage tumors across multiple anatomical sites, and thus deliver care of inferior quality owing to the constantly varying scope of services. In radiotherapy, for example, semi-automation of the treatment planning process may speed up treatment delivery, increase patient intake, and allow greater focus on the clinical nuances of patient management—all without requiring additional personnel. Although AI may not directly address diagnostic and therapeutic equipment shortages, AI integration into equipment design may help nontechnical operators troubleshoot issues when technicians are scarce. By analyzing historical maintenance data, AI may also help sustain long-term operations, predict failures, and avoid delay on parts and consumables.

The third application area relates to population health and allows public agencies to realize cause-and-effect relationships, appropriately allocate the often limited resources, and ultimately mitigate the progression of epidemics (8). Improving data collection in LMICs is central to these applications. For example, AI may help maintain up-to-date national cancer registries. Automated registry curation, by extracting standard data from free-form text found in radiology and pathology reports, may help reduce labor costs that account for more than 50% of all registry activity expenses (9). Other applications include identifying hotspots for potential disease outbreaks in unmapped rural areas by utilizing AI-powered analysis of aerial photography and weather patterns, as well as planning and optimizing CHWs’ household visiting schedules. Although these applications may prompt immediate actionable interventions, their translation into effective long-term health policies remains unclear.

HIC-based AI applications in health care are far from perfect. Most are at the proof-of-concept stage and require further demonstration of utility through clinical validation in prospective trials. The underlying methods are often uninterpretable, making it difficult to predict failures and critically assess results. Data used to train AI models are almost entirely collected within HICs, and models are hence skewed toward certain diseases, demographics, and geographies. With varying degrees of statistical data analysis and quality control, errors and systematic biases are introduced into models, thereby limiting their generalizability, especially when deployed in different contexts. Ethical concerns about the use of AI in health care include undermining patient data privacy protections and exacerbating the existing tension between providing care and generating profit, as well as introducing a third party into the patient-doctor relationship, which changes expectations of confidentiality and responsibility (10). From a regulatory perspective, medical malpractice and liabilities in health-related algorithmic decision-making are yet to be formulated. Nearly all AI tools in health care are single-task applications, and so they are incapable of fully substituting for health professionals. Understanding these limitations may help avoid hype and inflated expectations.

Artificial intelligence in resource-poor health care settings

The rise in incidences of cancer and other noncommunicable diseases is straining the limited resources and infrastructure in low- and middle-income countries. Artificial intelligence (AI) applications aimed at individual patient, health system, and population levels promise to enhance the access to and quality of care.


Introducing AI tools in resource-constrained settings presents additional challenges. The distinct needs, diseases, demographics, and standards of care in LMICs must be acknowledged through identifying specific use cases where AI involvement would have the greatest impact. Data for AI training and validation must be context specific: Computer vision systems may be required to work with legacy data formats (e.g., film versus digital x-ray), whereas developing chatbots will require compiling corpora in local languages. Solutions must also be context specific. For example, an automated system should not recommend treatments that are unavailable locally or are prohibitively expensive. Moreover, human factors should be considered: What levels of skill, education, and computer literacy are required of end users? The amount of behavioral change needed to raise awareness and confidence in AI systems should also be addressed, enabling users to recognize limitations and accurately interpret results. Infrastructure constraints should be assessed, including the availability of devices for serving AI applications, reliability of internet connectivity and bandwidth, electricity, and the amount and quality of existing digital data, as well as future digitization efforts.

Multiple digital initiatives have been proposed to enhance access to and quality of health care in LMICs. These include technologies to support health care practices using electronic processes (eHealth) and remote telecommunications (Telehealth), an example of which is mobile health (mHealth) using mobile phones and tablets. Best practices for scaling these initiatives in LMICs have been established on the basis of real-world experiences, including the World Health Organization’s mHealth Assessment and Planning for Scale (MAPS) Toolkit (11). These efforts could provide learning opportunities for similar digital AI applications. Many of the challenges faced by integrating electronic medical records in LMICs, for example, are likely to also impede AI applications, including limited funding, poor infrastructure for reliably delivering technologies, and discontinuous participation from users (12). Integration opportunities could also be considered: An existing mHealth application for patient-physician remote communication can be enhanced with an AI chatbot to triage patients prior to the consultation.

There is skepticism about the value of introducing AI in LMICs given the need to prioritize investments in basic infrastructure (13). AI-driven interventions should not be evaluated in isolation, nor should they be regarded as a universal panacea: Although sizable initial investments may be required, the marginal cost of providing an existing AI software service to one more user is minuscule, giving it economical scalability. An AI application may also use the deployment channels of existing digital technologies, making it almost readily deployable.

Ultimately, AI interventions in LMICs should be initiated, owned, and administered by local stakeholders—with HICs providing funding, expertise, and advice when needed. AI literacy may be included in existing global health educational programs to raise awareness about its capabilities and pitfalls. Empowering local technical AI talent will also be crucial, and may be accelerated through high-quality free educational online resources. AI implementation will require rethinking existing regulatory frameworks. For example, the training and scope of practice of CHWs may be expanded to include screening and diagnosing NCDs (14). Investment areas critical to bringing AI into LMICs must also be identified, as well as gathering evidence on the impact of AI solutions (15). Uneven distribution of access to technologies has created a digital divide between the rich and poor, while contributing to existing global inequalities. AI could emerge as a socially responsible technology with inherent equity.

References and Notes

Acknowledgments: Thanks to R. H. Mak and W. Ngwa for their valuable input. Supported by the U.S. National Institutes of Health (U24CA194354 and U01CA190234). A.H. is a shareholder of and receives consulting fees from Altis Labs. H.J.W.L.A. is a shareholder of Genospace and Sphera.

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22 ACLA NEWS Importance of Clinical Lab Testing Highlighted During Medical Lab Professionals Week

Washington, D.C. – In recognition of the 300,000 medical laboratory professionals across the country who perform and interpret more than 10 billion laboratory tests annually, the American Clinical Laboratory Association (ACLA) today joined the American Society for Clinical Pathology in celebrating Medical Lab Professionals Week.

“At a time when lab tests guide more than 70 percent of medical decisions and personalized medicine opens new windows to wellness, clinical lab professionals play an increasingly important role in today’s healthcare system,” said ACLA President Alan Mertz. “From early detection and diagnosis of disease to individualized treatment plans based on a person’s unique genetic makeup, clinical lab testing is key to improving healthcare quality and containing long-term health costs.”

Lab Testing and Cost Containment

As an example, Mertz pointed to the role of lab testing in controlling the cost of diabetes, a chronic disease that affects more than 79 million Americans and is projected to cost the health care system as much as $514 billion by 2025. Three laboratory tests – the fasting glucose test, the glucose tolerance test, and the hemoglobin A1c test – play a critical role in diagnosing pre-diabetes and monitoring the impact of changes in diet and other risk factors. Good control of blood glucose – detected by lab tests – can delay or prevent diabetes complications such as heart disease, stroke, blindness, kidney disease, amputation, and even death.

The chart below demonstrates how early detection and management of diabetes through regular lab testing can save tens of thousands of dollars when treating a single patient.

How Does Lab Testing Work?

Clinical laboratory testing plays an essential part in the delivery of quality health care. A physician or other clinician orders lab tests to diagnose, treat, manage, or monitor a patient’s condition. The process begins with the collection of a sample of blood, tissue, or other biological matter from the patient, which is then sent to the laboratory where it is uniquely identified and examined to make certain that it is appropriate for the testing ordered by the health care provider.Life Cycle of a Lab Test

Some tests are manually evaluated, while most are performed using technically advanced instrumentation. Labs employ teams of licensed, highly skilled medical professionals specially trained to perform the requested analyses. Once the testing is complete, the lab issues a report with the findings to the ordering clinician. When the healthcare provider receives and interprets the lab results, informed decisions can be made as to most appropriate treatment for the patient.

America’s clinical labs support hundreds of thousands of employees, with a talent pool comprised of pathologists, biochemists, medical laboratory scientists, phlebotomists, pathologists’ assistants, and other highly skilled medical staff. With 80% of the nation’s 322,488 clinical laboratories operating as small businesses, the sector is a significant contributor to local jobs and economies. More information on the value of laboratory testing can be found at

The ACLA is a not-for-profit association representing the nation’s leading national, regional, and esoteric clinical laboratories on key issues of common concern, including federal and state government reimbursement and regulatory policies. For more information, please visit or connect with ACLA on Twitter and Facebook.

Amazon’s roadmap for Alexa is scarier than anything Facebook or Twitter is doing

Amazon has big plans for its virtual assistant. One day, perhaps sooner than you think, Alexa will take a proactive role in directing our lives. It’ll interpret our data, make decisions for us, and summon us when it has something to say.

Rohit Prasad, the scientist in charge of Alexa‘s development, recently gave MIT Technology Review’s Karen Hao one of the most terrifying interviews in modern journalism. We know how dangerous it is to let bad actors run amok with AI and our data – if you need a refresher, recall the Cambridge Analytica scandal.

That’s not to say Prasad is a bad actor or anything but a talented scientist. But he and the company he works for probably have access to more of our data than ten Facebooks and Twitters combined. And, to paraphrase Kanye West, no one person or company should have all that power.

Hao writes:

Speaking with MIT Technology Review, Rohit Prasad, Alexa’s head scientist, has now revealed further details about where Alexa is headed next. The crux of the plan is for the voice assistant to move from passive to proactive interactions. Rather than wait for and respond to requests, Alexa will anticipate what the user might want. The idea is to turn Alexa into an omnipresent companion that actively shapes and orchestrates your life. This will require Alexa to get to know you better than ever before.

The idea of Alexa being an omnipresent companion looking to orchestrate your life should probably alarm you. But, for now, the work Prasad and the Alexa team are doing isn’t scary on its own merit. If you’re one of the eight or nine people on the planet who has never interacted with Alexa, you’re both missing out and not really missing out. Virtual assistants, today, are equal parts miraculously intuitive and frustratingly limited.

With one interaction, you’ll say “Alexa, play some music” and the assistant will ‘randomly’ select a playlist that touches the depths of your soul, as if it knew better than you did what you needed to hear.

But the next time you use it, you might find yourself in a three-minute-long argument over whether you wanted to listen to music by Cher or purchase a beige chair (with free two-day shipping).

From a consumer point-of-view, it’s hard to imagine Alexa becoming so useful that we’d come running when it summons us. But Alexa‘s primary mission will always be to gather data. Simply put: Amazon, Microsoft, and Google are all trillion dollar companies because data is the most valuable resource in the world, and Alexa is among the world’s greatest data collectors.

Once Alexa stops listening for commands and starts making suggestions, it means Amazon‘s no longer focused on building a handful of giant training databases comprised of data from hundreds of millions of users. Instead, it indicates that it’s focused on building millions of training databases composed of data gleaned from single individuals or very small user groups.

Hao’s Tech Review article continues:

Prasad’s ultimate vision is to make Alexa available and useful for everyone. Even in developing countries, he imagines cheaper versions that people can access on their smartphones. “To me we are on a journey of shifting the cognitive load on routine tasks,” he says. “I want Alexa to be a productivity enhancer … to be truly ubiquitous so that it works for everyone.”

This transition probably won’t feel like a giant leap in technology. It doesn’t take an expert to assume that the pushy version of Alexa will still stumble, struggle, and fail to accomplish language tasks that a four-year-old would understand.

But it’s not about creating a science-fiction experience that wows you. It’s about finding ways to get even more personalized data for Amazon by, literally, giving Alexa permission to start the conversation.

Everything changes the moment our relationship with a collection of algorithms goes from “Hey Alexa” to “Yes Alexa?”

The Global Clinical Data Analytics Market was valued at USD 3.974 billion in 2018 and is expected to register a CAGR of 33.07% over the forecast period (2019-2024).

The “Clinical Data Analytics Market” research report 2019 delivers comprehensive information about the market ranging from the establishment to the predictable growth trend. Clinical Data Analytics market report provides brief analytical data of the market contenders globally using advanced methodological approaches, such as SWOT analysis, production chain, cost, sales margin, financial details, recent developments. Clinical Data Analytics market report also offers market competitors that includes detailed company profiles along with company product specifications.

Market Overview:


  • The Global Clinical Data Analytics Market was valued at USD 3.974 billion in 2018 and is expected to register a CAGR of 33.07% over the forecast period (2019-2024). Clinical data analytics in the healthcare sector plays a prominent role in curing and predicting diseases, which increases the quality of care and nullifies preventable deaths, which occur due to the negligence of some chronic diseases.
  • Availability of new technology and software, such as the internet of things (IoT) and mHealth, has provided the patients with ease of access to a range of services. With the aid of these solutions, patients can track their health data and share it with their respective healthcare professionals for any further treatment.
  • The increasing number of healthcare groups are currently looking toward analytics solutions for effective population health management, which is expected to increase the demand for clinical data analytics over the forecast period.
  • Increasing security breaches and data theft, coupled with low internet penetration in developing countries, are expected to restrain the market over the forecast period.Get a Sample Copy of the Report –

    In Clinical Data Analytics Market Report, Following Companies Are Covered:

  • Allscripts Healthcare Solutions Inc.
  • Caradigm
  • CareEvolution, Inc.
  • Cerner Corp
  • Health Catalyst
  • IBM Corporation
  • Koninklijke Philips N.V
  • McKesson Corporation
  • Optum Inc.
  • Oracle CorporationClinical Data Analytics Market 2019 report will help the industry aspirants in arranging their strategies. The measurements offered in this report will be an exact and helpful manual to shape the business development. Additionally, will Provide historical data along with a future forecast and detailed analysis and also expected opportunities.

    Scope of the Report:

  • Clinical data analytics help physicians take care of a patient’s health conditions better, as well as aid in understanding the health status of the patients concerned. The technology can vary according to the data involved or the users of the information or the actions, which are at the discretion of decision makers, such as nurses, doctors, public health officials, senior management, etc. <Reasons for Buying Clinical Data Analytics Market Report:
    • This report provides pin-point analysis for changing competitive dynamics
    • It provides a forward looking perspective on different factors driving or restraining market growth
    • It provides a five-year forecast assessed on the basis of how the market is predicted to grow
    • It helps in understanding the key product segments and their future
    • It provides pin point analysis of changing competition dynamics and keeps you ahead of competitors
    • It helps in making informed business decisions by having complete insights of market and by making in-depth analysis of market segments

    For More Information or Query or Customization Before Buying, Visit at –

    Key Market Trends:

    Quality Improvement and Clinical Bench-marking to Hold Significant Share

    Healthcare professionals have always had a great deal of information they could use, but that data was not easy to access manually due to the huge sheer volume of the data. With the advent of digitization, the ability to deconstruct data in medical imaging for analyzing can cause a drastic change in the healthcare industry.

    Combining huge volumes and types of data along with the technological improvements for analyzing massive amounts of information is creating significant opportunities for improving healthcare quality across the globe.

    Leveraging technologies like big data and utilizing computer systems such as IBM Watson allows analysis of verbal expressions, degradation in handwriting, facial expressions in predicting the disease among a host of other functions.

    United States to be a Major Market

    As per the National Institutes of Health, in 2016, the United States alone accounted for 40% of clinical trials worldwide. The collected data that is being generated from different sources need to be studied and analyzed for chalking out strategies for effective population health management. Under the new rules by the US government for Medicare, hospitals and doctors will be subject to financial penalties under Medicare if they are not using electronic health records (EHR). Though the United States has always been known to be a pioneer in use of advanced technologies for treating patients, doctors and hospitals have been slow to replace paper records with electronic records. These steps are being taken to put these EHR’s to meaningful use. Enforcement of these new regulations is expected to provide impetus to the demand for clinical data analytics solutions in the region.

    The Research Document Will Answer Following Questions Such as:

    • What are the cutting-edge technologies responsible for driving the growth of the market?
    • What are the main applications of the market? What are the growth prospects to the market applications into the market?
    • At what stage of development are the key market products?
    • What are the shortcomings that has to face to become commercially viable? Is their growth and commercialization dependent on cost declines or technological/application breakthroughs?
    • What is the outlook for the industry?
    • What difference does performance characteristics of Clinical Data Analytics create from those of established entities?


Healthcare orgs reaching analytics maturity in 3 key areas

Most organizations surveyed for a new report said their primary method to determine ROI is financial returns and improvements, followed by clinical outcome improvement and staff efficiency.


Analytics solutions have been very effective toward improving clinical, financial and operational performance at healthcare organizations, according to a HIMSS Analytics and Dimensional Insight study of 109 senior healthcare executives.

The survey found nearly 85 percent of organizations that are leveraging analytics are doing so in multiple areas, with two-thirds leveraging analytics in all three areas spotlighted by the survey – financial, operational and clinical.

Organizations have the highest overall success rate (78 percent) with clinical metrics, with a particular focus on using analytics to reduce readmission rates, infection control and reduction, and patient outcome improvements.

Analytics is seen as an extremely important component to organizations’ future strategy, with operational efficiency and cost management ranking as the top reasons for implementing analytics across all three areas.

Most organizations surveyed said their primary method to determine ROI is financial returns and improvements, followed by clinical outcome improvement and staff efficiency.

Survey results, however, indicated that these organizations observe their highest measured success rate (75 percent) if they use clinical outcomes improvement as their primary metric.

Budgeting and forecasting is the most employed metric across all three areas, used by roughly 90 percent of all survey respondents, followed closely by financial performance tracking and reporting, financial benchmarking and trending, and revenue cycle management measured success rates were also highest in these areas.

When implementing and leveraging an analytics solution at their health system, survey respondents said improvement to patient care (safety, quality and outcomes) was their primary goal in deploying clinical analytics solutions.

For financial analytics, increasing revenue and cost management were top concerns, followed by operational efficiency, and for operational analytics, driving efficiency and improving decision-making capabilities was the goal most often cited by respondents, followed by increased revenue and cost management.

“As healthcare organizations move to value-based payment models, they are finding that focusing on clinical metrics, including readmission rates, infection control, and patient outcome improvements is critical for success,” George Dealy, Dimensional Insight’s vice president of healthcare solutions, said in a statement. “Analytics provides tremendous insight into these areas and can benefit healthcare organizations that are navigating this transition.”

“Healthcare analytics has often focused on measuring financial improvement or staff efficiency. And while those are certainly important focus areas for hospitals and health systems, clinical outcomes improvement is critical, especially as value-based payment models take hold,” the report concluded. “Those organizations that are focusing on clinical outcomes improvement as their key measurement for ROI report seeing the greatest measured success from their analytics implementations.”

Quest Diagnostic Network (GDN) Enhancing diagnostic insights to address worldwide health challenges and improve local patient care

Healthcare systems around the world are designed for local service delivery, yet diagnostic innovations and discoveries are occurring daily around the globe. These breakthroughs can remain localized and unshared.

The Global Diagnostics Network (GDN) is a strategic working group of diagnostic laboratories, each committed to unleashing and sharing local innovation to increase global access to diagnostic science and services — ultimately generating diagnostic insights and enhancing global healthcare.

GDN Members

GDN members are some of the world’s leading diagnostics companies across the globe, operating at the highest professional standards. Collectively, this worldwide community of 10 healthcare companies has a presence in countries with two-thirds of the world’s population, and over 90% of the global pharmaceutical market.

GDN Logo.jpg V 2.0

Quest Diagnostics Launches the Global Diagnostics Network (GDN) to Improve Healthcare by Increasing Access to Diagnostic Insights Around the World

The GDN brings together leading diagnostics services providers to share expertise and drive worldwide diagnostics innovation Secaucus, NJ, October 10, 2018 –Quest Diagnostics (NYSE: DGX), the world’s leading provider of diagnostic information services, announced today jointly with other leading diagnostic services providers the formation and launch of the Global Diagnostics Network (GDN). The GDN is a strategic working group of diagnostic laboratories,each committed to unleashing and sharing local innovation to increase global access to diagnostic science, information, and services-ultimately generating enhanced diagnostic insights to improve the delivery of global healthcare. Collectively, this worldwide community of healthcare leaders has a presence in countries with more than half of the world’s population across all continents. In addition to Quest Diagnostics,GDN charter members are Al Borg Medical Laboratories,Dasa, GC Labs,KingMed Diagnostics, Primary Health Careand SYNLAB.“The Global Diagnostics Network will help solve some of the world’s most pressing healthcare challenges by enabling fast and consistent access to leading diagnostic innovations and best practices,”said Steve Rusckowski,Chairman, President and CEO, Quest Diagnostics. “As we met with regional lab leaders over the past year, it became clear that we can accelerate the development and delivery of advanced diagnostics and diagnostic insights by bringing together our peers and sharing best practices.”GDNinitiatives will benefit patients, healthcare providers,referring practitioners, pharmaceutical innovators, government agencies, non-governmental organizations (NGOs), and academic institutions. Starting priority areas of focus include a global launch platform for high quality companion diagnostics and the creation of an emerging pathogen preparedness network to expedite infectious disease research and response. Additional initiatives will be rolled out based on regional and global priorities.“The GDN collaboration will open up new opportunities by removing silos around unique knowledge and resources,”commented Carrie Eglinton Manner, Senior Vice President, Advanced Diagnostics, Quest Diagnostics.“We see this as a pragmatic approach to driving growth and excellence for all network members, while providing customers global access to innovations in diagnostics science, information, and service delivery.”For more information about the GDN, please

About Quest Diagnostics

Quest Diagnostics empowers people to take action to improve health outcomes.Derived from the world’s largest database of clinical lab results, our diagnostic insights reveal new avenues to identify and treat disease, inspire healthy behaviors and improve health care management. Quest annually serves one in three adult Americans and half the physicians and hospitals in the United States, and our 45,000 employees understand that, in the right hands and with the right context, our diagnostic insights can inspire actions that transform lives., Quest Diagnostics, and all associated Quest Diagnostics registered or unregistered trademarks are the property of Quest Diagnostics. All third-party marks are the property of their respective owners.

Google: Follow Our Structured Data Requirements to Ensure Rich Result Eligibility

Google’s John Mueller recommends following the company’s official structured data requirements to ensure content is eligible to be displayed as a rich result.

This topic was discussed in the latest installment of the #AskGoogleWebmasters video series in which the following question was addressed:“[Do] we need to use structured data as per the Google Developers site (including required/recommended properties) or can we use more properties from apart from the Developers site?”

In response, Mueller says it’s perfectly fine to use structured data properties that aren’t listed in the Google Developers site. Structured data that’s listed on the Developers site is what Google officially supports as rich results – there are numerous other types available for webmasters to use.

With that said, if the goal is to have a web page be displayed as a rich result in Google, then following the company’s official requirements is highly recommended.

Though it’s important to keep in mind that utilizing structured data does not guarantee that a web page will be displayed as a rich result, it simply makes it eligible to be displayed as a rich result.

Using structured data types outside of what Google officially supports is optional, but also acceptable. Even if the structured data is not supported in the form of rich results, it still helps Google understand the content better and rank it accordingly.

Here’s how Mueller explains it:“Independently, you’re always welcome to use structured data to provide better machine readable context for your pages. Which may not always result in visible changes, but can still help our systems to show your pages for relevant queries.”

Google is Testing Search Results Without URLs – We may be looking at the beginning of the end for URLs in search results.

Google appears to be testing the complete removal of URLs from search results, displaying only the website name instead.

This was spotted by a Reddit user who shared the following screenshot in a thread:

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For comparison, here’s how part of that search result appears when URLs are shown (I’ve circled the difference to make it painfully obvious).

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Google has slowly been moving away from showing full URLs since the introduction of breadcrumbs a few months ago. Now it seems Google is testing the impact of removing URLs altogether, to the point of not even showing the domain name.

As one Reddit user states in the thread, perhaps the greatest concern about this change is being able to verify the legitimacy of the website being shown n search results:

“In this era of search results that don’t even show the domain name, how’s Google going to keep phishing sites from using the names of the businesses they’re trying to impersonate? Worse get, might Google have to roll this back after discovering phishing sites were able to exploit this lack of domains in the search results to get people to divulge passwords, credit card numbers, and all other sorts of sensitive information?”

Looking at it another way, removing URLs from search results likely won’t change much in terms of SEO and may even alter the perceived value of exact-match URLs.

Here are another Reddit user’s thoughts on the matter:

“I don’t think this is going to change much in terms of SEO, it just removes one factor that separated good content from good ranking. This is going to normalize the perception of users between traditional tld’s and modern tld’s because they cant subconsciously decide whether or not to click on something based on the perceived trustworthiness of the URL.

I think that fundamentally it is a good thing for new domains trying to get into a niche. this is going to devalue the perceived “vanity” of any particular url, which is good in a climate where basically any URL is taken.”

We may be looking at the beginning of the end for URLs in search results.


Google Search: Ongoing changes to the SERP design, directing more traffic towards paid advertisements

That means over half of Google’s search engine results lead to no clicks through to websites or properties that aren’t owned by Google.

Google no click results1

This data was attained via Clickstream and first analysed by SparkToro.

Why are fewer people clicking from the SERPs?

This Google organic search trend milestone has been caused by a number of factors:

  • An increase in search engine results page (SERP) features that pull information from external websites directly into Google’s search results. E.g. featured snippets and People Also Ask boxes.
  • Google related properties and features appearing for an increasing number of verticals (Google Flights, Google Maps, YouTube etc.)
  • Ongoing changes to the SERP design, directing more traffic towards paid advertisements

How does this impact my website?

Fewer searches resulting in clicks could mean fewer website visits. This could lead to a decrease in organic traffic, despite an increase in organic visibility or ranking position.

Measuring the performance of a site will be impacted, as traffic and conversions have traditionally relied on users moving from the SERPS to a site.

The extent to which this impacts traffic and conversion is highly dependent on vertical, however, as Google continues to expand its reach, there are lessons that can be learnt for SEO strategy moving forward.

What can we do about this?

With fewer clicks available than before, the organic landscape is more competitive than ever. But organic search still represents a significant opportunity. There are several things that can be done to make sure a website not only survives, but thrives in this new search environment:

  1. Optimise for Google’s SERP features

While some search results may lead to no clicks, being present in the SERP can have other benefits, like brand visibility and keeping your website at the forefront of a searcher’s mind. Crucially, optimising a site for Google features also means that competitors are not taking advantage of the same opportunities.

When investigating which SERP features to target, it’s important to prioritise. A site may choose to focus only on keywords that have a high click through rate (CTR), or may go after increasing visibility rather than clicks; optimising for features even if it results in a reduced CTR from that SERP. Be aware of the landscape and make informed decisions.

  1. Optimise content for Google-owned properties

Making sure a website is in a position to capitalise on traffic from Google-owned properties will help a brand stay visible, even if organic clicks are reduced. For example, a travel company may want to ensure they’re included in Google Flights. And websites with a physical location will want to be optimised for Google’s Local Pack.

  1. Optimise for search journeys

Searches rarely occur in silos; so even if one search results in no clicks, a future search may drive a visit. Ensuring that a website is set up to answer a user’s need at every stage of the search journey will give it the best chance of driving qualified leads.

It’s not enough to just offer a product as the organic search environment gets increasingly competitive and dominated by Google itself.

  1. Continue to build content that places users at the centre of a website experience

Organic search is often a significant part of any marketing campaign; however, SEO is not a silver bullet. Building a product and an experience that customers love is crucial to keep people coming back.

It’s worth noting that Google is currently being investigated by the US Department of Justice over antitrust claims relating to its search results. While we would never recommend relying on external factors like this, it’s worth noting that discussions are ongoing.

Should we target other search engines if Google results are resulting in fewer clicks?

Many SEO campaigns focus attention primarily on optimising for Google.

Despite Google offering fewer clicks from its search results than ever before, it’s still the largest search engine by a significant margin. This means that small gains in Google are equivalent to large increases in smaller search engines.


Google is at the forefront of search engine technology, with other search engines often playing catch-up. Optimising for Google will future-proof a website.


This article was written by Dan Cartland from The Drum and was legally licensed through the NewsCred publisher network. Please direct all licensing questions to