Today’s medical devices feature the most cutting-edge technology and sensors to improve patient health, from Fitbits that track heart rate during exercise to devices that can test and display blood glucose levels on a smartphone. Healthcare professionals have also welcomed the use of smart devices and tablets to enhance hospital or clinic visits, lower costs and reduce medical errors.
The demand for health informaticists grows substantially with every government push to adopt technology and ease the switch from paperwork to electronic health records (EHR) systems. To ensure the next generation of health informaticists are learning the skills needed to adapt as technology advances, many universities are offering a health informatics degree program that emphasizes hands-on learning in health IT, data analysis and the healthcare system.
Here’s a look at what a formal education in health informatics looks like today, and what in-demand skills employers can expect from health informaticists down the road:
Health Care System Analysis and Assessment Outcomes
Improvements to the healthcare system begins with a thorough understanding of what the current system lacks. Today’s health informatics courses allow students to examine healthcare needs and analyze the supply and distribution of health professionals and facilities. These courses also explore current industry pain points, particularly care costs, how to assess care quality, and the financial models of care used in both private health insurance systems and government programs.
Health informatics students are also familiarized with methods for determining quality of care and the economic impacts of health care models. Courses examine the outcomes and value added from the view of patients and providers, with a focus on determining standards for setting organizational policy.
Health Care History and Implementation of EHR Systems
To understand the role that health informatics plays in improving the healthcare system, students also cover the history of the U.S. healthcare system. By exploring current trends in electronic health records – including social, ethical, economic and cultural impacts of choices – students will be prepared to identify what improvements can be made to EHR systems later in their careers as health informaticists.
Guest post by Carl Wright, general manager, TrapX Security.
Carl Wright
In August 2015, my colleague Moshe Ben Simon contributed an Electronic Health Reporter story about how hospitals can protect against data breach using deception technologies. Since then, TrapX Labs, the research and development group within TrapX Security, has seen substantial evidence that cyber attackers have continued their attacks on healthcare targets. The number of attacks, quantity of data stolen and the sophisticated human attackers that TrapX Labs continues to track are increasing quarterly. Out of the top seven data breaches of 2015, three of them (Excellus BlueCross BlueShield, Premera Blue Cross and Anthem) lost more than 100 million records combined.
On Jan. 4, 2016, the Identify Theft Resource Center (ITRC) reported that 66.7 percent of all records breached came from the healthcare industry. Healthcare continues to be targeted because of the high value of the data and the vulnerabilities healthcare institutions are susceptible to, such as the medical device hijack (MEDJACK). More information on MEDJACK can be found here.
The convergence of this healthcare cyberwar with incomplete HIPAA compliance creates a double jeopardy situation for healthcare professionals. Not only must healthcare institutions deal with the damage inflicted by a cyber attacker and then manage the data breach penalties, but they also face investigation and additional penalties from HHS. Hospitals, accountable care organization (ACO) networks, large physician practices, health insurance companies, diagnostic laboratories, radiology/skilled nursing facilities, surgical centers and others are high value targets for attackers and all face these risks.
Training is Essential
New strategies to prevent healthcare data breaches have evolved in many areas. Regular training for both clinicians and non-clinicians can have a positive impact on reducing successful attacks.
Clinicians and non-clinicians need to recognize that their “connected” healthcare environment needs to be tightly controlled. IBM’s “2014 Cyber Security Intelligence Index” noted that 95 percent of all security incidents seem to involve human error. Even a MEDJACK usually starts with an email or website based attack. Assuming a healthcare organization’s network perimeter and internal defenses are properly configured and updated, the next step a healthcare organization should take to substantially reduce its risk is implement a rigorous employee training program.
The first component of training comes during orientation. New employees typically receive passwords and authentication information from information technology (IT), the help desk and supervisors in their area, and it’s imperative they manage them in a safe manner (no yellow sticky notes, please).
Over the past few years, healthcare technology has seen many advances. We’ve achieved mass-market adoption of EHRs, many organizations are making meaningful progress on data aggregation and warehousing information from multiple diverse systems, and wearables and other sensors show much potential to unlock personal information about each patient. The pace of change in healthcare is quickening, with each new technology or initiative sending off a chain of reactions across the entire ecosystem, ultimately improving patient care.
I see three trends driving the industry toward change:
Analytics will help predict population heath management
One of the persistent industry challenges is the “datafication” of healthcare. We’re amassing more and more data now than ever before. And new sources (like wearable devices) and new health factors (like DNA) will contribute even more. This data explosion is putting increased pressure on healthcare organizations to effectively make this data useful by delivering efficiency gains, improve quality of care and reduce overall healthcare costs.
Navigating this digitized healthcare environment will require increasingly sophisticated tools to help handle the influx of data and make the overload of healthcare information useful. In 2016, the industry will begin to take concrete steps to transition to a world where every clinician will see a snapshot of each of their patients to help them synthesize the critical clinical information they need to make a care decision. Moreover, hyper-complex algorithms will allow providers not only to know their patients, but to accurately predict their healthcare trajectories. By giving providers insights into how each patient is trending, clinicians will be able to make better-informed, precise decisions in real-time.
Consolidation leads to new healthcare models, improved outcomes
New models for effective population health management continue to drive change across healthcare systems. These models incentivize stakeholders to optimize costs, identify organizational efficiencies and improve decision-making processes to deliver better care at a lower cost through an emphasis on care coordination and collaboration.
Guest post by Will Hayles, technical writer and blogger, Outscale.
Will Hayles
We tend to conceive of the Internet as a place of human communication. In reality, a significant proportion of the traffic carried over the networks that comprise the Internet is generated by machines talking to other machines. For the most part, there is no human in the loop of these so called machine-to-machine (M2M) interactions. Data is gathered from sensors attached to devices which are connected to the Internet. That data it is stored and analyzed in the cloud. Only at the end of the process is a human involved, once the deluge of data generated by machines has been squeezed down to extract useful information.
To take a simple example of how machine-to-machine processes can deliver useful information to human decision makers and system designers, consider a pet store that specializes in selling tropical fish. The store has several dozen aquariums filled with sensors that report the nutrient content and chemical composition of the water — data that is stored on a cloud platform. Another system records the store’s purchases, stock levels and waste. An analytics solution designed by the store’s developers takes both sets of data and tries to develop feeding and water treatment regimens that reduce waste (dead fish) and increase yield (fish growth). Every day, workers at the store get a list of tasks generated by the system — perhaps one of the aquariums is slightly too acidic and action needs to be taken or waste will increase.
The bulk of the communication is machines talking to machines, the culmination of which could be a text message that instructs the fish store owner to add three drops of a particular chemical to a specific tank.
Now that you have a basic grasp of the fundamental idea of M2M communication, let’s focus on how it is being used in the healthcare sector to improve patient outcomes and increase spending efficiency.
Information Sharing
Healthcare treatments often involve many different professionals, from general practitioners to specialists, and from radiologists to physiotherapists. Complex cases can require input and decisions from a dozen or more individuals across several institutions. To be effective, it’s essential that healthcare professionals have access to up-to-date and comprehensive information about the case. With paper record keeping, it’s all too easy for information to fail to reach the right person at the right time. M2M systems, in which relevant data, including test results and real-time monitoring, are made available to all stakeholders simultaneously and automatically can make a real difference to healthcare outcomes, radically increasing the efficiency and efficacy of treatment regimes.
Remote Patient Monitoring
Remote patient monitoring is the classic case for M2M communication. With the advent of sensor-equipped medical devices with internet connectivity, patient status can be monitored in realtime, with physicians and other healthcare professionals receiving alerts when a decision or action needs to be taken.
It isn’t that doctors aren’t skilled, intelligent or capable enough—it is that the demands being placed on them are too great.
Time and documentation demands mean that something has to give. As many physicians have pointed out over the years of the HITECH Act’s implementation, the thing that normally “gives” is facetime with patients: actual, hands-on delivery of care and attention. Instead, they are driven to input data for documentation, follow prompts on EHR interfaces, ensure their record-keeping practices will facilitate correct coding for billing, as well as tip-toeing around HIPAA and the explosion of security and privacy vulnerabilities opened up by the shift to digital.
The reality of modern medicine—and especially the rate at which it evolves, grows, and becomes outdated—means that doctors need what most every other industry has already integrated: more brains. Not simply in the form of EHRs for record-sharing, or voice-to-text applications as a substitute for transcriptionists, but as memory-supplements, or second brains.
As a species, humans are also evolving away from memory as a critical element of intelligence, because we now have devices—“smart” devices—always on, always on us, and always connected to the ultimate resources of facts and data.
Our smart devices—phones, tablets, etc.—are gateways to the whole of human knowledge: indexes of information, directories of images, libraries question and answer exchanges. In effect, we are increasingly able and willing to offload “thinking” onto these devices.
Supplement or Supplant?
Depending on the context and application, this trend is both helpful and potentially harmful. For those prone to critical thinking and equipped with analytical skills, offloading some elements of memory to these devices is a question of efficiency. Even better, the more they practice using it, the more effective they become at integrating devices into their cognitive tasks. In others (those less prone to think critically), it is a shortcut that reduces cognitive function altogether: rather than a cognitive extension, the devices act as substitutes for thinking. Similarly, increasing over-reliance on the internet and search engines further diminishes already deficient analytical skills.
The standard roadmap for a medical education entails a lot of memorization—of anatomy, of diseases, of incredible volumes of data to facilitate better clinical performance. It isn’t memorization simply for the sake of recitation, though; it is the foundation for critical thinking in a clinical context. As such, medical professionals ought to be leading candidates for integrating smart devices not as crutches, but as amplifiers of cognition.
So far, that has been far from the dominant trend.
Enter the Machine
Integrating computers as tools is one thing, and even that has proven an uphill battle for physicians: the time and learning curve involved in integrating EHRs alone has proven to be a recurring complaint across the stages of Meaningful Use and implementation.
Patient engagement—another of the myriad buzzwords proliferating the healthcare industry lately—is another challenge. Some patients are bigger critics of the new, digitally-driven workflows than the most Luddite physicians. On the other hand, some patients are at the bleeding edge of digital integration, and find both care providers and the technology itself moving too slowly.
Guest post by John Squire, president and chief operating officer, Amazing Charts/Pri-Med.
John Squire
As president and COO of a leading electronic health record (EHR) and practice management (PM) provider, part of my job is to be in constant communication with providers about health IT. They tell me and my team what works for them and what doesn’t work; what brings joy to their practice and what keeps them up at night. All this insight helps polish my crystal ball, making it clear what we can expect to see in 2016:
EHR system will pivot from regulatory compliance to physician productivity. EHRs are generally blamed for fueling the professional dissatisfaction of physician. A few software vendors are looking at the problem-oriented medical record (POMR), a more intuitive approach that works similarly to the way a doctor thinks. It organizes clinical records and practice workflows around specific patient problems, making it faster and more satisfying for physicians to use.
The problem list not only delivers a “table of contents” to clinically relevant issues, but also gives a provider a longitudinal view of a patient’s healthcare over time. This intuitive method of information organization makes it easier for provider and patient to set the agenda at the start of the exam. During the exam, the POMR supports the nonlinear nature of a patient encounter.
The POMR also helps reduce cognitive overload, which can lead to medical mistakes such as misdiagnosis and other potentially life-threatening errors. Providers can see “bits” of data like lab results associated with a specific problem, thus easing the number of mental connections required to make sound medical decisions.
Chronic care management (CCM) will grow quickly because it makes sense for both patients and providers. Our healthcare system is changing to address the needs of an aging population with chronic illnesses like hypertension, diabetes, heart disease, and more. To promote the effective care coordination and management of patients with multiple chronic illnesses, the Centers for Medicare and Medicaid Services (CMS) introduced CPT code 99490. This code reimburses providers for remote, inter-visit outreach, such as telephone conversations, medication reconciliation, and coordination among caregivers.
The reimbursement for CCM services is an average of $42 per month for Medicare beneficiaries. New levels of technology integration will enable clinicians to complete CCM reporting of remote care from inside their EHR system.
Guest post by Amy Sullivan, vice president of revenue cycle sales, PatientKeeper.
Amy Sullivan
The multi-year run-up to the ICD-10 cut-over last October had a “Chicken Little” quality to it. There was prolonged hand-wringing and hoopla about the prospect of providers losing revenue and payers not processing and paying claims – the healthcare industry equivalent of “the sky is falling.”
Then CMS helped calm things down by announcing last July (as the AMA reported at the time), “For the first year ICD-10 is in place, Medicare claims will not be denied solely based on the specificity of the diagnosis codes as long as they are from the appropriate family of ICD-10 codes.”
Since ICD-10 is all about specificity – the number of diagnosis codes increased approximately four-fold over ICD-9 – this was a big relief to all involved. And, if you believe new research data, the sky indeed has not fallen: Sixty percent of survey respondents “did not see any impact on their monthly revenue following Oct. 1, 2015… Denial rates have remained the same for 45 percent of respondents. An additional 44 percent have seen an increase of less than 10 percent.”
Still one has to wonder what will happen after Oct. 1, 2016, when the current leniency expires and ICD-10 code specificity is required. Will physicians be in a position to enter their charges completely and accurately once “in the general neighborhood” coding no longer suffices?
They will if their organization has invested in technology that adheres to best practices in electronic charge capture system design. The three watch-words are: specialize, simplify and streamline.
A charge capture system is specialized when it exposes only relevant codes to physicians in a particular specialty or department, and when it provides fine-tuned code edits. With different types and processes of workflows (and let’s face it, personal preferences), physicians need an intuitive and personalized application that easily fits into their individual work styles. A tailored user experience allows providers to build and display their patient lists in whatever way is most convenient and meaningful to them – down to lists organized by diagnosis and “favorites.”
Guest post by Steve Tolle, chief strategy officer, Merge Healthcare, an IBM Company.
Steve Tolle
The volume of health-related data available to physicians and other healthcare providers from disparate sources is staggering and continues to grow. In fact, a 2014 University of Iowa, Carver College of Medicine report projects that the availability of medical data will double every 73 days by 2020. Such data overload can make it difficult for clinicians to keep up with best practices and innovations.
Perhaps because imaging is so pervasive in healthcare, the medical imaging field has turned to data analytics and cognitive computing to help clinicians use large volumes of data in a meaningful way. These decision-support tools help them manage data to improve patient care and deliver value to referring physicians and payers.
At RSNA15, the crowds packed presentations on data analytics and cognitive computing and flocked to vendor exhibits featuring these decision-support tools — indicators of their expanding role in healthcare. In years past, exhibit space was primarily devoted to showcasing new imaging modalities.
Interest in analytics is growing rapidly as the U.S. health system transitions from volume- to value-based payment models — models that challenge physicians involved in medical imaging to demonstrate value. Physicians are under pressure to deliver educated, accurate, useful and efficient interpretations even as imaging studies become increasingly large in size and complex in scope. And these physicians are expected to communicate this information quickly and in a user-friendly manner. As a result, clinicians are turning to analytics-based solutions to boost efficiency and enhance the quality of their service to help them deliver the value demanded by payers, referring physicians and patients.
