Emerging Trends in Healthcare


As a marketer, part of my job is to keep my finger on trends in the market. I often find myself asking “What are our customers thinking about?”, “What problems are they trying to solve?” and “What are the next big moves in healthcare that will help improve outcomes?” Here are a few things that caught my eye that I think are important to track.

3D Mammography and Automated Breast Ultrasound

3D Mammography or Digital Breast Tomosynthesis (DBT) is still in the news. A recent article points to DBT as an emerging trend. A year ago, I would have agreed with that statement. It was a very hot topic at RSNA 2014 and still the subject of many presentations and vendor exhibits in 2015. However with direct to consumer marketing and rapid adoption in the last 18-24 months, this technology has gone from being a hot trend to becoming standard practice. In many institutions, patients are now having their 2nd, 3rd or even 4th or breast exam done with tomosynthesis.  Are there institutions yet to adopt DBT? Of course but now it is much more of a must have than a leading edge technology.

The next wave in women’s health care will be wide-spread adoption of automated breast ultrasound for woman with dense breasts. While again not a new technology, the adoption rate has been slower than 3D mammograms. Even though many women have heard about the benefits of DBT and are demanding it, women are not often aware that they are affected by dense breasts.  They must be told by their doctor that they are a candidate and must be educated on the reasons why this is important. Consumer education will help speed the adoption of automated breast ultrasound and help bring it into the mainstream.

Data Consolidation and Interoperability

Data consolidation is a topic that arises whenever you talk to CIOs and other healthcare information technology executives. Electronic Medical Records (EMRs) have been consolidating the data from the patient’s clinical record for some time. Vendor Neutral Archives (VNA) and Enterprise Content Management systems (ECM) are being implemented to consolidate images, pictures and other documentation in standard formats such as DICOM, CDA (Clinical Document Architecture) and XDS (Cross Document Sharing) formats.  These systems make it possible to bring together images and information from multiple institutions and multiple disciplines – radiology, cardiology, gastroenterology, dermatology and more – making them accessible to the physicians and patients via a direct viewing application or by integrating the viewing app with the EMR. Integration with the EMR puts it all in one place while storing them in standard formats makes them viewable by applications that use the standards.

While the use of VNAs to consolidate data has been talked about for many years, the acquisition and implementation is just starting to take off in the US.  Europe and Asia are further ahead. Adoption there is accelerated by government mandates to consolidate data across a region or a country, making it accessible to any physician treating the patient.

However, new technologies will drive a shifting in our thinking. In addition to moving the data to a single storage location, data consolidation can be accomplished by bringing information from multiple systems together in a “virtual” sense. New standards like Fast Healthcare Information Resources (FHIR) are being developed by HL7 that will help consolidate data from multiple systems no matter where that data resides. FHIR provides an alternative to document-centric approaches by directly exposing discrete data elements as services. For example, basic elements of healthcare like patients, admissions, diagnostic reports and medications can each be retrieved and manipulated via their own resource URLs[i]. Imagine the impact on care when data is made available through consolidation in the EMR, a VNA and directly using technologies like FHIR. This takes access to information to a new level!

Putting Interoperability to Use Improving Access

At the recent HIMSS conference in Las Vegas, interoperability was one of the big buzz words. While you might say that this is not really an emerging trend – heck, in the 1980’s I was working on system interoperability with HL7 interfaces – there are new possibilities that are worth exploring.

HL7 interfaces are great for passing data from one system to another for storage. Once committed to the database, the data is there for viewing, to incorporate into decision support logic and to help make decisions on patient treatment.

What if we could do more with interoperability? In his 2004 presentation at SCAR, Dr. Kevin McEnery said “Radiologist workstations must evolve to include ancillary clinical data sources critical to the radiologist.”[ii] That meeting was 12 years ago and we are still struggling with this issue!

In a 2014 study[iii], neuroradiologists at Froedtert Hospital were asked to review 2,000 head CT cases along with data from the EMR that was provided by the emergency physician. They found that in 13% of the cases, absence of the EMR data would have resulted in a negative impact on patient care. In another 22% of the cases were very likely or possibly influenced by the EMR data.

Sure this data is available to the radiologist but they have to go find it. What if we could bring data from multiple systems together – without moving it from one system to another – and automatically present it to the radiologist, cardiologist or other clinician so that he or she could deliver a better diagnosis and better recommendations to the their patients? With tools and techniques like FHIR, web services and machine learning, it is possible to go find data in external sources like an EMR and present it to the radiologist on their desktop as they are reading the exam. And that information should be relevant and in context. A pediatric radiologist reading an X-Ray of a child’s leg probably does not need to know that the patient is on blood thinners to diagnosis a sarcoma but a neuroradiologist reading a head CT with a bleed might need this information. Adding ontologies that help to normalize data to the machine learning tool sets will make it possible to get the right information to the right radiologist at the right time.

3D Printing in Healthcare

A potentially disruptive technology in healthcare is 3D printing. While not widely used today, 3D printing is finding its way into the news as it is used to augment care decisions and create cost-effective, customizable and personalized implantable and non-implantable devices.

As a specific example, in 2013, a man used 3D printing to help his wife’s physicians come up with a diagnosis and treatment plan for a tumor that was causing severe headaches[iv]. After two MRIs and two different interpretations from two different radiologists, the woman was given a treatment plan involving radical surgery with a high probability of negative side effects. Her husband decided to do some work on his own.  Fortunately, he was in the 3D printing business so he had more technical knowledge than most of us.  After obtaining DICOM copies of his wife’s MRIs, he was able to use several software programs and a 3D printer to build a physical rendering of his wife’s tumor that they sent to multiple hospitals.

One of the hospitals who reviewed the model agreed to take on the case. The surgical plan devised with the use of the 3D printed models was much less invasive than previous recommendations. Instead of “sawing into her skull and lifting her brain”, the doctors planned to go through her eyelid. Recovery was fewer than three weeks! While the 3D models were not the only reason that everything went so smoothly, it was a new way for physicians and surgeons to approach diagnosis and treatment.

In a more recent example, Alder Hey Charity Hospital was the first UK hospital to use 3D printed models in the surgical suite.  In the new state of the art Research, Education and Innovation Centre, Alder Hey used 3D printed models during children’s spinal surgery.[v]

3D printing not only allows healthcare teams to create personalized medical devices, it has the potential to drastically reduce the cost of these devices. SLS 3D Ltd and Replica 3DM in the UK are partnering to bring personalized healthcare to patients in the National Health Service (NHS).[vi] They plan to use 3D printers to make high-resolution surgical models for surgeries under tight deadlines.

Replica 3DM has been working with the NHS for almost five years, making 3D printed models from refined DICOM data from CT and MRI scans. These are used by surgeons to carry out pre-operative planning. In one recent hip surgery, the model was able to reduce the overall cost of the procedure by an estimated £3,500 or $5,000 USD – savings that add up quickly.

And the US government has not ignored 3D printing. The FDA has regulations designed to approve and manage medical devices that are created using 3D printing.[vii]

How big is this market expected to grow? One firm projected that the 3D printing market in healthcare will grow at a CAGR of 15.6% from 2014 through 2020, estimated to be $1.3 billion by 2020[viii].

While these are only a few of the emerging trends in healthcare, there are many more innovations directed at improving patient care and saving money.  What do you think the next big trend will be?


[i] Fast Healthcare Interoperability Resources, Wikipedia

[ii] EMR integration with PACS enhances information access; Erik Riley, AuntMinnie, May 2004

[iii] EMR access can be critical to radiology decision-making; Mike Bassett, May 2014

[iv] How a man used 3D printing to help treat his wife’s brain tumor, Rex Santas, Mashable, Jan 14, 2015

[v] Alder Hey Is First UK Hospital to Use 3D Printed Model in Operating Theatre, Michelle Matisons, 3DPRINT.com, March 21, 2016

[vi] SLS 3D Ltd. & Replica 3DM Partner to Bring Personalized Healthcare to UK with 3D Printed Surgical Models, Bridget Butler Millsaps, 3DPRINT.com, March 18, 2016

[vii] FDA Clears OPM’s 3D Printed Spinal Implants; Michael Molitch-Hou; July 2015

[viii] Healthcare 3D Printing Market To Grow At A CAGR Of 15.6% From 2014 To 2020, Grand View Research, March 2016

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