Guest post by Gavin Robertson, CTO and senior vice president, WhamTech.
As technology continues to permeate healthcare in different ways, it is becoming increasingly important for providers to have access to the data generated and retained by these technologies. With insurance providers, hospitals and clinics using a variety of electronic health records (EHRs), patient portals and databases, it can be difficult for all providers to have access to all relevant and most recently updated patient information. Differences among EHR vendors and systems make data access, sharing and interoperability nearly impossible.
Interoperability is a hot topic in healthcare today. Healthcare providers want to move beyond conventional Healthcare Information Exchanges (HIEs) that generally exist as single application to single application (P2P) data formats. The HL7 standard data model has helped a lot, but (i) it is too complex and extensive for full adoption, (ii) it is, typically, a specific relational or hierarchical implementation, requiring additional transformation, and (iii) there are a number of implementation variations.
Regardless of the improvements associated with the HL7 standard data model, the challenges facing interoperability remain; in that (a) multiple vendors have multiple ways to represent common data, (b) data may be required from more than one application and associated data sources, (c) poor data quality, (d) there may be no unifying view of data from one or more data sources, e.g., single patient view, and (e) there is no ability to write back to/update data sources.
HL7-based FHIR (Fast Health Interoperability Resources) APIs is a recent attempt to standardize access to data sources, but most vendor systems are nowhere close, as it is a different way of representing data from most vendor data schemas; i.e., object vs relational data representation. Also, some FHIR APIs need access to multiple tables in a single data source or in multiple data sources.
To implement FHIR APIs, one approach is to convert between the data source schemas (relational, hierarchical or flat) and the FHIR object model on-the-fly, but it does not address other shortcomings (poor data quality, no federation and lack of master data management (MDM)/single patient view). Another approach, which improves on just converting formats, is to copy and transform data into FHIR-friendly data stores and enable data services on top. However, this introduces additional problems, including latency, security, privacy, no interactivity; e.g., no write back/update to operational systems, additional storage and systems, and time and cost to implement.
Regardless of the approach, FHIR APIs open up interoperability and raise capabilities to new levels. New workflows can be developed and run using simple power end-user applications, such as BPM, reporting, BI and analytics tools. Examples include new smartphone app-driven BPM workflows running against FHIR API services, include write back/updates, on multiple legacy data sources in multiple organizations. Another example being hybrid cloud installations where multiple data sources are both on premise and in the cloud.
Guest post by Sean Hughes, EVP managed document services, CynergisTek.
Healthcare has spent a significant amount of both human and financial capital addressing the security of their environments over the last several years – but have we forgotten a major vulnerability?
Printers and print-related devices (e.g. copiers, fax machines, scanners, etc.) continue to be a major component of our infrastructure and a big part of our clinical and business workflows, yet in most organizations, they continue to represent a gaping hole in our defenses. The advent of the EHR has not equated to the perceived reduction in print, but rather some research shows it’s responsible for an 11 percent increase in print in healthcare over the same time as the implementation of this technology. This increase in print volume brings with it an increase in the number of devices required to process the paper.
The approach most organizations have taken related to the security of these devices falls into one of two categories: segmentation of the network or reliance on manufacturers for “secure” devices. These approaches vary significantly from the approach most organizations have taken for other endpoint computing devices and leaves an organization open to the possibility of negative outcomes.
The industry has seen an increase in the computing power of these devices (e.g. internal hard drives, scan to file or application, residual data on devices, mobile printing, USB-enabled device access, etc.) and the bad guys are aware of this. More and more we see stories in the news of print devices being used as entryways for bad guys to circumvent our protections and put our data and our organizations at risk. According to an article published by BBC News in February 2017, “Hacker Briefly Hijacks Insecure Printers,” a hacker was able to access more than 150,000 printers that were briefly left accessible via the web.
The most effective way to address this threat is to treat these devices no differently than all our other data endpoints, be it a desktop, server, or any other piece of infrastructure. We need to look at these devices and ensure they meet the same security standards.
The most effective way to mitigate risks starts with knowing what the risks are. The first step should be a comprehensive printer fleet security assessment that is part of your overall security program. This can be accomplished either through your internal processes or by engaging a competent third party. Either way, you need to know what you don’t know, and you need to know it now.
The results of that assessment will drive the remediation efforts as well as define the ongoing measures our organizations should take. These steps will be directly related to the vulnerabilities identified but will most likely fall into the following categories:
Regardless of whatever business you operate, the end goal is always customer satisfaction and healthcare is no different. Since healthcare is particularly valuable, it makes sense that the financial reward given to a valuable service should be high and based on a value model.
However, value-based models in healthcare do not have the same outcomes as they do in other businesses.
Value-based payments have their advantages and disadvantages. For instance, on the one hand, value-based systems effectively liberate physicians from the constraints of fee for service so that they can concentrate on the overall health of their patients. Alternatively, some people say that value-based payment systems impose unneeded extra pressure on providers without necessarily getting the job done.
What is value-based payment in medicine?
Value-based systems reward physicians and healthcare providers with incentive payments for the quality of care given to patients with Medicare. These payment systems are part of a strategy to improve how healthcare is delivered and paid for. The purpose of any value-based system is to:
Improve how patients are given care in hospitals
Improve the overall health of the population
Lower the overall cost of healthcare
Effectively, value-based systems move toward paying doctors and healthcare providers based on the quality of care rather than the quantity of care given. Instead of charging patients based on the number of visits and tests that they order (fee for service payments), today, more hospitals are charging based on the value of the care that they give.
Fee for service payments
Traditionally, healthcare providers are refunded by third-party payers like insurance firms or by the government through Medicare or Medicaid. The amount of money that is paid is set at a going rate that is typically established by the agencies themselves. Since the budgeting of the costs and expenses are based on third party consumers, the system is marred by administrative hiccups, which has led to runaway care costs at the expense of the quality of care given and the patient.
The difference between fee for service and value-based payments lies in reimbursements and the quality of care provided.
In 2017, the healthcare industry is continuing to discuss the major shifts that are taking place around a patient’s financial responsibility. While it’s important to acknowledge that these discussions have been largely driven by significant changes to reimbursement models, it’s imperative that we look beyond this single causation. Financial responsibility involves more than simply making and collecting payments.
Consider these figures:
Consumer payments to healthcare providers nearly doubled from 2012 to 2015
84 percent of employers have increased or say they plan to increase insurance deductibles and/or co-pays for their plan participants
As a result, healthcare providers are managing much higher transaction volumes from patients, which is causing them to rethink their approach to collecting payments. A new approach requires a deep dive into payment security, PCI scope, EMV and workflow changes – a challenging proposition to say the least.
To understand these challenges and how healthcare providers are responding, let’s take a look at some of the top revenue cycle trends in the industry.
Key Revenue Cycle Trends
Bloomberg recently reported that the second-biggest for profit U.S. hospital chain revised its Q4 2015 provisions for bad debt up by $169 million. Forty percent, or $68 million, was from patients that were unable to pay deductibles and co-payments.
In a similar fashion, J.P. Morgan’s Key Trends in Healthcare Patient Payments report pointed out that the rate of bad debt for insured patients is increasing by over 30 percent per year at some hospitals.
Not surprisingly, these and other challenges stemming from increased patient responsibility are dominating the 2017 revenue cycle management agenda for senior finance executives in healthcare. Among the top revenue cycle initiatives for 2017 are several related to payments:
Preventing or resolving underpayments
Lowering the cost to collect patient payments
Increasing collection rates among insured patients
Collecting more balances at the point of service
Understanding the role of payment technologies
At its highest level, payment technology plays an important role in advancing revenue cycle initiatives and addressing increases in patient responsibility. Most importantly, the technologies offer patients easy and secure payment options at multiple touchpoints within the continuum of care.
Below are a few of the ways payment technologies help healthcare institutions achieve their revenue cycle objectives while improving the patient experience:
A critical aspect of clinical research today is patient-centered studies, which provide the insights that empower doctors, clinicians, and patients to make better informed care and treatment decisions. But the challenge is building an effective system to gather and share data across multiple systems and empower researchers and stakeholders within health-focused organizations to easily compare different types of interventions, conduct pragmatic clinical research and translate the benefits of that research into medical practice.
The Louisiana Public Health Institute (LPHI) has implemented a system that has accomplished this lofty goal.
LPHI’s work focuses on uncovering complementary connections across sectors to combine the social, economic, and human capital needed to align action for health. It champions health for people, within systems, and throughout communities.
The primary challenge lies in integrating a seamless data workflow across health systems and integrating network activities into the work of existing clinical teams, and a workflow that is flexible to meet each organization’s specific clinical and research needs. There is also a great need for onboarding participating staff members who can help educate patients about a study and set realistic expectations around the trial. The technology they use needs to enable physicians and patients to make informed decisions in real-time.
The non-profit organization created (and now serves as the coordinating center of) a Clinical Data Research Network, REACHnet, that increased the capacity of regional learning health systems to conduct patient-centered clinical studies. This network centers around a robust data infrastructure with a patient engagement platform for study recruitment, data collection and connection to clinical records.
REACHnet addressed these key aspects by designing the network with the following principles:
Targeted patient engagement – The patient engagement infrastructure of REACHnet uses a web- or tablet-based platform in examination rooms, which is electronic medical record (EMR)-agnostic. These web- or tablet-based platforms, developed by Persistent Systems, are pre-loaded with the pragmatic trial app suite (PTAS) that facilitates patient engagement with targeted educational and research content. Through what is termed as the Health in Our Hands (HiOH) patient network, the PTAS facilitates patient enrollees to engage with the research studies and programs. The dashboard provided by PTAS is currently equipped with visual graphics, charts and analysis to show patients how the study is progressing and how it could affect them. If required, the dashboard can also provide the possible treatment options and the current evidence available.
The application suite is user-friendly and allows patients to interact with the study easily, share data and access information both inside and outside the clinical settings via a personal patient portal. Enrollees receive health information, research results and opportunities to participate in new studies through the HiOH patient network. This continuous engagement between the patients and the researchers, clinicians and doctors ensures the longevity of participants’ interest in the studies, which is crucial to the success of clinical research.
Data access and seamless integration into workflow – The basis for a learning health system is conducting pragmatic research that requires healthcare organizations to embed clinical research into the workflow of healthcare delivery systems rather than just organize them in controlled conditions. Data is gathered at multiple sites within the network and sent to the REACHnet data center. Data collected at each site, whether inside or outside a clinical setting is associated with a Global Patient Identification (GPID) system that matches patients without the sharing of identifiable information. Selected information is pulled in by REACHnet for conducting various studies and comparing different interventions based on these GPIDs.
Since REACHnet uses a common data model (CDM) to prepare retrospective and prospective research and prep-to-research queries, PTAS can quickly sync to data in the CDM to push targeted content to patients. This completes the engagement loop across a clinical workflow and provides access to data in real-time.
It’s impossible to see the future with certainty, but one branch of technology is playing a leading role in helping institutions and industries predict, on the basis of empirical research, the future behavior of participants and the outcomes of their decisions.
This relatively new branch of tech – predictive analytics (or PA) – has made inroads at a steady clip in the marketing, manufacturing and financial services industries. It is now gaining traction in healthcare as well.
Although debates around its ethical applicability to healthcare persist – the debate around data privacy, for one – the consensus emerging across the board is that with the right skills and in the right hands, PA has the power to effectively address challenges in the healthcare ecosystem in ways that human intelligence alone cannot.
Let us examine a few recent examples.
The power of PA
The Gold Coast Health Hospital in Southport, Queensland, Australia, dramatically improved patient outcomes and hospital staff productivity by applying a predictive model that was able to project with 93 percent accuracy emergency admissions before they happened. By analyzing admission records and details of sundry circumstances that led to patient admission to the ER, hospital staff were able to know how many patients would be coming in, on any day of the year, what they would be coming in for and methodically plan procedures that were now for all purposes elective rather than urgent.
Similarly, the El Camino hospital in California was able to drive a dramatic turn-around in its high rate of patient falls by collaborating with a tech company. The company, Qventus, linked patient EHR to bed alarm and nurse call light usage to derive an algorithm that was able to alert nurses in real time about the high-risk patients under their care and the exact times when they were most likely to be vulnerable. The result was a whopping 39 percent reduction in falls, improvement in patient health outcomes and a dramatically improved reputation for the hospital.
In fact, it isn’t only hospitals that are alive to the potential of analytics. Tech companies too are cognizant of how some of the newest technologies being developed under their roofs have immediate relevance to healthcare outcomes. In a paper published earlier this year, researchers associated with Google demonstrated how deep learning algorithms were able to correctly identify metastasized cancer tissue with nearly 90 percent accuracy as compared to just 73 percent when done by a human pathologist.
The need of Telemedicine AHA Report shows that 20 percent of US citizens are located in the rural areas and do not have access to the healthcare professionals and their services. The industry, though, has found a way out in form of telemedicine.
Telemedicineis defined by American Telemedicine Association as the process of medical data exchange from one site to another via electronic devices in order to improve patient clinical health status, electronic devices meaning emali, applications, video, wireless gadgets, smartphones, etc.
Telemedicine notion includes three main modalities: real-time, store-and-forward, and remote patient monitoring. The first modality means doctor-patient interaction with the help of audiovisual technology. The second — transmission of patient data and her history via secured electronic channels to a healthcare specialist. The third — collection of the patient data with the help of special devices (like wearables) and its transmission to a healthcare provider.
Foley predicts that by 2020, telemedicine will grow to 36.2 billion US dollars at CAGR (compound annual growth rate) of 14.3 percent. In 2014, it was 14.3 billion US dollars. Currently, there are around 200 healthcare academic centers in the US that provide video consultations worldwide, according to American Telemedicine Association.
Foley has also reported that 90 percent of healthcare top minders have already begun telemedicine integration. Nearly 70 percent of employers are going to offer telemedicine services as perks for their employees. 42 states in the US have already created more than 200 legislative acts about telemedicine.
US patients are not opposed to the idea of telemedicine, too. According to American Well, 64 percent of them would attend a meeting with their doctor via telecommunication means; forecasts that there will be 7 billion telemedicine users worldwide.
Types of Telemedicine
Telemedicine deals with many spheres of healthcare: telestroke (remote data transferred to the emergency specialists on site), teleradiology (images and media transfer), tele-ICU (systems and networks connected to the critical medical specialists), telemental health (distant mental health treatment), cybersurgery (operations held by surgeons remotely with the help of telecommunication and robotic instruments), and telepharmacy.
Importance of Telepharmacy
As stated by Centers for Disease Control and Prevention, 74.2 percent of physician visits involve drug therapy. During hospital outpatient department visits, there were 329.2 million drugs ordered or provided. These numbers demonstrate the potential hidden in the telepharmacy. Moreover, the number of independent pharmacies is steadily decreasing from 2011.
Telepharmacy was originally introduced for the rural areas that lack the resources to supply existing demands in the pharmacy. However, it’s now being actively used by healthcare systems, regardless of the location, due to its ability to meet medication needs 24/7.
A vivid example of telepharmacy success, Comprehensive Pharmacy Services, has launched telepharmacy project, CPS Telepharmacy, that works 24/7 the whole year round. It has been reported to detect and improve 1,300 medical errors per year and to have reduced around 45 percent of costs, with improved quality.
CPS Telepharmacy can process nearly 3 million medication events a year and involves with 200 medication orders a day (73,000 cases a year). Averagely, errors occur 3.6 times per day. Those can be wrong patient, wrong dose, or wrong medication.
CPS Telepharmacy has brought substantial improvements in form of reduced costs, lowered adverse drug events, and improved clinical outcomes.
PiplineRX, another leader in the industry, has recently announced its round funding at $9.1 million U.S. dollars by McKesson Ventures, Mitsui & Co Inc., and AMN Healthcare. Currently, the system is available in around 200 hospitals.
Many reports have been issued emphasizing the importance of the control of antibiotics prescription, namely ASP (antimicrobial stewardship program) to prevent emerging antibiotic resistance. Advanced technologies are to help reduce costs on drug by finding cheaper alternatives or preventing over prescribing of medications.
To sum up, telemedicine, as well as telepharmacy, have great perspectives. The number of their supporters in the healthcare industry is increasing from day to day and is not going to stop.
Guest post by Joanna Gorovoy, senior director product and solutions marketing, Axway.
The healthcare industry is in the midst of digital transformation. At the same time, heightened government regulation, evolving healthcare policies and a rise in healthcare consumerism are driving a shift toward value-based, outcome-driven care models.
The focus on maximizing value and outcomes requires organizations across the healthcare ecosystem to work together, especially across a variety of different, and often unaffiliated organizations, including hospitals, health insurance companies, pharmacies and wearable health tech companies. Additionally, data silos and interoperability issues make it difficult to derive value from health data across ecosystems, provide quality patient care and optimize health outcomes.
Healthcare IT leaders in today’s digital era face a great opportunity and a daunting challenge: deriving value from massive volumes of healthcare data while meeting heightened demands for data privacy and security. In 2016 alone there were 106 major healthcare data breaches, exposing 13.5 million individuals’ records. As healthcare data breaches continue to rise in numbers, healthcare IT leaders must reevaluate how they approach key initiatives across patient engagement, population health management and care coordination.
They need to provide secure and innovative digital experiences by implementing application program interfaces (APIs), which are a set of routines, protocols and tools for building software applications, and increase awareness of industry standards, such as Health Level Seven International’s (HL7) Fast Healthcare Interoperability Resources (FHIR). Doing these two things will provide assistance in addressing interoperability issues and simplify the exchange of health information across the ecosystem.
But it doesn’t stop there. Moving toward a future where healthcare data is more widely accessible will require greater security management across all organizations that have access to patient data. To create a more secure and scalable foundation for digital innovation in healthcare you must follow these three steps:
Guest post by Ken Perez, vice president of healthcare policy, Omnicell.
On May 23, the Department of Health and Human Services (HHS) released a report on individual market premium changes from 2013 to 2017 for the 39 states using the federal government’s healthcare.gov platform. The report provided a good gauge of the affordability of the ACA marketplaces.
The HHS report found that all 39 states experienced increases in individual market premiums since 2013. Average premiums rose during the four-year period by 105 percent, which translates to an average annual premium increase of $2,928. To put the 105 percent premium hike in perspective, it was more than 20 times the growth in the Consumer Price Index (CPI) and more than eight times the nation’s healthcare inflation over the same period. While 16 states had premium increases under the national average of 105 percent, 20 states had premium increases between 105 percent and 200 percent. Moreover, three states—Alabama, Alaska and Oklahoma—saw premiums triple, rising more than 200 percent.
A comparable analysis of the 11 states running their own marketplaces has not yet been conducted, but from 2016 to 2017, their average approved individual market rate increase was 19 percent, over nine times CPI growth and over five times U.S. healthcare inflation.
Multiple interrelated factors have driven these premium increases, including lower-than-expected enrollment, as estimates ranging from 12 million to 15 million people—disproportionately young and healthy—who were expected to enroll in the marketplaces by the end of 2016 did not do so. Because of the lower-than-expected enrollment and relative non-participation by the young and healthy, the marketplaces have been left with an older, sicker risk pool, producing huge losses for many health plans, in spite of the previously mentioned substantial premium increases. Consequently, in 2017, 80 insurers left the ACA marketplaces while 11 entered, yielding a net decrease of 69.
The inordinate premium inflation of the marketplaces reflects a cycle that appears to be worsening, so much so that some have described it as a “death spiral.” As health insurers exit the marketplaces, competition decreases, which naturally leads to premium hikes, as well as to a narrowing of plan choices. The higher premiums and fewer choices dissuade people from signing up or cause current enrollees to drop out, further shrinking the risk pool.
If the ACA marketplaces prove to be unsustainable then access to affordable healthcare plans for millions of Americans—regardless of the availability of premium credits—will be at risk.
During the past few months, hospital organizations have lobbied for changes to the American Health Care Act (AHCA), with core concerns about possible growth of uncompensated care resulting from increases to the uninsured population and separately, the popularity of high-deductible plans with many of the insured, which raises concerns about patients’ ability to pay their hospital bills. However, as the recent HHS report compellingly points out, hospitals should not only be worried about possible ramifications of the AHCA—the serious, fundamental weaknesses of the ACA’s health insurance marketplaces constitute a clear, present and increasing challenge to hospital finances.
Being born with a heart condition I have had a chance to see how healthcare has evolved or stagnated in innovation because of inherent risk to the bottom line. Reducing revenue, patient risk and pressure from big pharma and insurance has kept the status quo. It’s crazy to think that we can order food from our phones and yet can’t even schedule our appointments online at most physicians offices and hospitals. We have the most expensive and least effective healthcare system in the world, it’s broken so we need to fix it.
There is a lack of technology in healthcare as a whole. Think about when you go into a doctor’s office and you tell them what’s wrong or if you go to a hospital and nurses are tracking your symptoms, they still write it on a piece of paper at most hospitals and physicians offices! Well, what happens when the nurse or doctor can’t read what’s been written or worse what if that paper gets lost. To put that in perspective, hospital errors are the number three leading cause of death in the U.S.
Where there is some technology it is often difficult to use and is not standardized so if you go to an emergency room that doctor will likely have to spend time trying to get your primary care doctor on the phone to better understand how to care for you. It’s happened to me before, the ER doctors spent hours trying to track down my cardiologist to get a rundown on what medications or tests need to be run on me, all the while I was lying there in pain waiting for care. Standardization of basic medical protocols needs to happen. Even better, a shared database of all the different medical protocols and AI can run through to find the right match or machine learning like autocorrect and predictive typing on your phone.
Too much data
Today’s doctor and healthcare providers receive copious amounts of data, whether that’s from your daily activity data, your daily measurements, data from scans, DNA testing data, etc., that they must go through in order to properly diagnose a patient. Sometimes there’s too much data for the doctors to consider and so they cut bait with some of it to rank all the clutter. On top of all that data they are looking into a system to find how that data correlates with your back pain, sleep issues and whatever another symptom you are looking at then finding the proper medication for you. All of this takes time away from the doctor to properly develop a relationship with the patient and better diagnose patients problems. Let’s dive into how machine learning and AI’s can help with this.