From genomic-directed clinical trials and gene editing to personalized medicine and more, recent innovations in science and medicine are helping patients live longer, healthier and fuller lives.

Behind these advances are experts including genetic counselors, biomedical engineers, precision medicine champions, medical informaticians and others who will continue pushing their fields forward in service to patients.

In this three-part series, BWH Clinical & Research News caught up with several BWHers about these “careers of the future,” and what they will mean for clinical care and for those looking to join the health care workforce in the years ahead. Read part two of the series below and part one here.

Precision medicine: Big data and a new paradigm for health care

Jeffrey Golden

Jeffrey Golden

After Jeffrey Golden, MD, joined the Brigham as chair of the Department of Pathology in 2012, he quickly began working to create a program in what he called “computational pathology” to better understand the data that pathologists were delivering to clinicians on behalf of patients.

BWHC President Betsy Nabel, MD, asked Golden to think about these questions more broadly, beyond pathology, and charged Golden and Joseph Loscalzo, MD, PhD, chair of the Department of Medicine, with establishing a precision medicine program at BWH.

The National Institutes of Health defines precision medicine as “an emerging approach for disease treatment and prevention that takes into account individual variability in environment, lifestyle and genes for each person.” Most of precision medicine today is based on genomics and understanding changes in the genome, says Golden.

“For example, we’re looking at genetic mutations that initiate or drive cancer,” said Golden. “We have many drugs that target those mutations, and once identified, we can try to treat these patients on an individual level. This is also true for patients with other diseases, such as cardiac disorders and pulmonary and neurologic disease. Patients can be treated in different, more personalized ways based on our understanding of genomics, which has led to significant medical advances.”

Golden says that one of the major challenges of precision medicine is that most patient data exist in silos. Imaging data are not integrated with pathology data on patient reports, for example. During the course of a 20-minute patient visit, a clinician will review numerous types of data – possibly imaging, pathology data, EKG results, family history and information from a patient exam – to make a decision. Yet studies show that clinicians can only integrate about five data points at a time; thus a lot of data is not effectively utilized.

However, building algorithms that could interrelate all of the data and combine and analyze them in a more sophisticated way will likely yield much more information about a particular patient, and over time, entire patient populations. The teams that will make this possible, Golden says, will consist of computational scientists, computer scientists and mathematical modelers working closely with pathologists and clinical teams to build the infrastructure needed for this work.

“We want to be able to intervene and treat the right patient at the right time in the right setting, ideally before disease occurs,” said Golden. “This creates a different paradigm for medicine in which we use all of our patient data not as independent variables but as an integrated knowledge base. Ultimately, this is a fundamental change in how we deliver health care.”

Clinical informatics: Interpreting and recognizing patterns in big data


Li Zhou

Li Zhou, MD, PhD, lead investigator in the Division of General Internal Medicine and Primary Care and senior medical informatician in Clinical Informatics for Partners eCare, bridges the worlds of medicine and informatics every day.

Informatics is broad and multidisciplinary, says Zhou, but her branch, clinical informatics, is the application of informatics and information technology to health care.

Zhou’s first foray into informatics was after graduating from medical school in Shanghai, China, in the late 1990s. In an effort to better understand health and disease in women and children, the Chinese government had established a national health information management system to which health care institutions reported their data. Zhou helped train health care professionals to use the system in four provinces. Soon after that, she moved to New York to pursue her master’s in computer information systems and then – during the information technology boom of the early 2000s – her doctorate in biomedicine informatics, where she focused on natural language processing and temporal reasoning, two subareas of artificial intelligence.

In 2007, she joined BWH and Partners Information Systems and worked alongside researchers and clinicians to build new features and support tools into BWH’s homegrown medical record systems.

“We managed data elements, the building blocks for composing templates, for clinicians to write notes,” said Zhou. “We also created clinical decision support services, including alerts and warnings, to help improve patient care.”

In addition to this operational work, Zhou began pursuing her own research and built the Medical Text Extraction, Reasoning and Mapping System (MTERMS) to extract information from free text entries and clinician notes in the electronic medical record, and normalize and code the data for better analysis and future clinical decision support. Zhou and her colleagues were able to mine 20 years of allergy data using the system and create more defined and standardized terms for clinicians to describe allergies and adverse reactions in the medical record.

“If we have well-defined terms, the information can be coded, which leads to better analysis later,” said Zhou. “It also allows for interoperability, which means I can exchange data between hospital A and hospital B, and both hospitals will understand it because they use the same terms.” Zhou is also using MTERMS for other research, such as medication reconciliation, family history, social-behavioral information and malpractice claims.

Personal devices may offer a glimpse into future directions for the field of clinical informatics. Speech recognition technologies, such as Siri and Dragon, are being examined for application to health care in the form of medical note dictation and more. Zhou is currently at work on developing a detection system to identify speech recognition system errors to improve the quality of documentation.

In her eyes and others’, the future of clinical informatics is bright.

“I am constantly reading and learning to keep up with new technology and thinking about how to use different aspects of different fields in my work,” said Zhou. “It is so exciting to use technology to create solutions to make patient care more safe and effective. Every day I learn something new because information technology continues to grow so much.”