It has been estimated that over 150 countries have been impacted in some ways by the WannaCry worm that went on a widespread attack over the weekend. Pieces Tech’s VP of Security, Slava Gomzin, shares his perspectives in our latest blog-post along with recommendations on how you can protect yourself.
Malware like the WannaCry worm is very difficult to detect before it actually strikes your computer. The good news is that this particular strain of WannaCry has been stopped, but, much like the flu, there’s a good potential for a different variation to be out in cyberspace, very soon. So… what exactly is it? And what should you do?
What is Ransomware and WannaCry?
Ransomware is type of malware that can infect your server or workstation and encrypt all data files which will disable your computer. Ransomware demands a ransom payment in order to decrypt the files and make the system operational again.
WannaCry is a ransomware that affects Windows machines which don’t have recent security updates. After infecting a Windows computer, WannaCry will automatically encrypt all of its files and ask for the Bitcoin equivalent of $300-$600 in order to decrypt them. If ransom is not paid, WannaCry will eventually delete all files permanently. The ransomware spreads by scanning other computers linked to the infected machine and attacking them through a vulnerability in Microsoft systems.
What to do if you find you’re vulnerable
If you haven’t already install this Microsoft Windows patch immediately, and if you do encounter the popup message, do not click “check payment” or “decrypt” buttons. Most importantly, never pay ransom. Your payment does not guarantee that the lost files will be restored and it will not prevent your computer from future attacks.
3 Steps for Preventing Ransomware Attacks
Following best practices for security is easy and can keep your computer safe from future ransomware attacks. Gomzin recommends taking the following steps as a best practice.
- Keep your Windows systems up to date by enabling Windows auto updates
- Install an anti-malware solution and keep it up to date
- Regularly backup your files and systems or/and store all your files in folders that are synced up with cloud drives such as Google Drive, Microsoft OneDrive, or DropBox, so in case of a ransomware attack, you are able to format your drive, reinstall Windows, and restore your data files.
More information about ransomware and WannaCry attack basics can be found here.
About Slava Gomzin
In addition to information security enthusiast and full-stack technologist, Slava Gomzin is the VP of Security at Pieces Technologies. He is the author of multiple publications on information security including books Hacking Point of Sale (Wiley, 2014) and the just-published Bitcoin for Nonmathematicians (Universal Publishers, 2016).
This year, we’re coming to SXSW to meet with the influencers and leaders who can shape the next era of integrated healthcare.
Pieces Iris™, our platform for coordinated case management, connects social service organizations and healthcare providers – finally addressing the social and economic determinants of health in a patient’s care regimen.
That means better care. And healthier communities:
- 360-degree care management: Managing all the determinants of health outcomes
- Closed-loop referrals: Connecting providers and services directly
- HMIS for the homeless: Providing full-fledged homeless management
- Intuitive, cloud-based design: Offering an easy and accessible tool for all users
On average, we could spend $5.11 on swag for each SXSW visitor. Or, we could use that money to do some good.
At our booth, Pieces Tech will donate $5.11 on your behalf – all you have to do is show up and choose which of our five partner organizations you’d like to support. Then we’ll introduce you to Pieces Iris™, which is reshaping the way care providers and local organizations work together – to the benefit of our communities.
Find us at SXSW
Register for SXSW
There’s still time. SXSW is spectacular and inspiring – you won’t regret it.
As clinicians, we are all first and foremost scientists. We have been trained on the “scientific method” – to clearly understand the problem, the hypothesis, the interventions, the outcome, and the implications for future study. That has been drilled into us in school, in training, and in every piece of evidence that we lovingly read in our daily news feeds. We place so much emphasis on the evidence when it comes to treating our patients, however, I am not sure that we place enough emphasis on the evidence as it applies to our healthcare programs.
The Problem: Rising Healthcare Costs
National healthcare spending has been increasing every year. Recent results released by CMS at the end of 2016 shows our national 2015 healthcare expenditure at $3.2 Trillion or $9,900 per person and accounting for 17.8% of GDP. As you might expect, the largest share of the bill is paid by the US government with Medicare picking up $646B and Medicaid paying for $545B. Close behind the US government in healthcare spending is the private insurance plans picking up $1072B. In last place are the consumers, with a paltry of out of pocket spend of $338B.
The Intervention: Programs to Rein in Cost
With these huge dollars poured into healthcare, it is not surprising that CMS has implemented numerous programs to shift our payment system from fee-for-service to value-based care. Instead of paying hospitals and providers for the number of visits and tests they order, now payments are based on the value of care that they deliver. The value-based programs have included a variety of hospital programs including Hospital-Acquired Condition Reduction Program (HACRP), Hospital Readmissions Reduction Program (HRRP), and Hospital Value-Based Purchasing (HVBP) Program. As we head toward the new programs coming up this year, such as the Alternative Payment Models (APMs) and Merit-Based Incentive Payment Systems (MIPS), it is a good time to glance back at the programs that have been in place and consider how well they have been working.
The HVBP Program is one program that brings both quality of care and cost of care into the program. Hospitals in the program have 2% of their reimbursement withheld and then hospitals are gauged on measures such as mortality, HACs, patient safety, patient experience, efficiency, and cost reduction. Based on the hospitals total performance score, they may earn back their 2% and have the opportunity to earn an additional 2%. At the moment, the program is cost neutral as far as healthcare expenditures, so the main question becomes: Is this financial incentive for hospitals really improving care?
Is HVBP Program Improving Outcomes?
A 2015 study looking at the early effects of the HVBP program compared 12 clinical processes and 8 patient experiences measures between hospitals enrolled in the program and hospitals excluded from the program. This study looked at a 1-year time period from 2011 to 2012. The study found no significant difference in any of the clinical processes or the patient experience measures during the first implementation period of the program. The authors concluded that the financial incentives were too low to incentivize hospitals or that the complicated design might not have given them a clear target. The results suggest that we have yet to find the right measures to improve clinical process and patient experience.
In 2016, a new study investigated if HVBP was indeed improving care by comparing mortality for AMI, heart failure, and pneumonia between hospitals participating in the program and hospitals excluded from the program. This study was conducted over a 5-year period from 2008-2013. The study found that mortality trends between the two groups was small and non-significant. In looking at subgroups of hospitals, including poor performers, there was still no association between HVBP Program participation and better outcomes. The authors concluded that we have yet to find the right quality metrics and incentives to improve patient outcomes.
The Bottom Line
As we apply the scientific method to the HVBP Program, one would question if the program is targeting the right outcomes or providing the right incentives? What interventions have the high performing hospitals implemented that may be contributing to their better outcomes? Are there other factors that CMS should be targeting? For example, hospital ownership and social determinants are now shown to play a role in improving outcomes, yet they are not currently factored in HVBP program design or measures. So, as any good scientist would conclude, the HVBP “experiment,” so far, is inconclusive. Scientific method would dictate that the next step is to put our subject back under the microscope and test new hypotheses.
Pieces Technologies will be visiting HIMSS 2017 with one goal:
Change how healthcare organizations see their data.
With the proliferation of healthcare data and metrics comes confusion:
- Which data really matters?
- How can we turn big data and AI and into tangible decision-making?
We’ll show you at the event. For now, let’s start with some important HIMSS resources.
Where we’ll be at HIMSS 2017
|MONDAY, FEB. 20 – 10:30AM | 320 | Chaplin Theater
Shattering the Glass Ceiling: Lessons Learned for Aspiring Female Executives
CEO Ruben Amarasingham, moderator | Learn More
|MONDAY, FEB. 20 – 10:00AM – 6:00PM | Booth 7785-07 | Innovation Zone
|TUESDAY, FEB. 21 – 9:30AM – 6:00PM | Booth 7785-07 | Innovation Zone
|TUESDAY, Feb. 21 – 2:00PM | Booth 7785-07 | Innovation Zone
AI Approaches for Throughput Management | Learn More
|WEDNESDAY, Feb. 22 – 9:30AM – 4:30PM | Booth 7785-07 | Innovation Zone
VR headset giveaway
We’re going to back up our promise by allowing visitors to see everything differently – with VR. Our booth’s VR experience will surprise you, and it’ll give a stark demonstration of the concepts behind Pieces Decision Support.
Plus, by coming to our booth and chatting, you could walk away with your own VR equipment. Come see us at booth #7785-07!
HIMSS schedule and map
Let us take the complexity out of this whirlwind event. Check out the map below to locate us in the Innovation Zone, and visit the full HIMSS map and detailed HIMSS schedule to get a better handle on your experience.
Do it! The world’s best healthcare minds are waiting for you.
Skip the line
You can cut to the chase right now and schedule a demo of Pieces Decision Support, our solution for turning clinical data into informed clinical decisions and interventions.
By Sanaz Cordes, MD
Sometimes friends, clients, students, or family ask: “Don’t you miss being a real doctor?” I always pause for a moment when I get this question. I’ve given up explaining that being a doctor is like being a Marine…. you kind of always get to “be” one, even after you’re no longer in active service. I once even had a local newspaper reporter refer to me as “Sanaz Cordes, a ‘former’ physician,” in an article that I’m certain no one read.
But, I must admit that, as I work with health tech startups, I long for the ability to be teleported back to the early 2000’s (we’ll leave the backdating vague), when I was actively practicing inpatient and outpatient medicine. When I think about the “tools” we used back then, as compared to what’s available now, I long for a “do-over.” Some of my biggest frustrations with practicing medicine centered around the piecemeal, manual workflow of caring for patients.
At the time, the shiniest object in the room was the EMR! Almost no practices, and very few hospitals, had EMRs for charting, CPOE, or even results viewing. I remember carrying spiral Mead “memo” pads and a pencil while walking back and forth between the hospital’s nurses’ station, the patients’ rooms, and the floor secretary’s desk dozens of times each night! Saturday morning rounds used to take upwards of 4 hours. I always fretted missing a critical lab result, forgetting to place an important order, leaving out relevant chart notes for consultants, or even forgetting to see a patient altogether! And, sadly, one of these things would normally occur weekly.
It’s sometimes hard for me to comprehend the types of tools and technologies that are available for physicians today. I spent most of 2014-2015 focusing on the physician shortage in this country. Most of the early solutions were centered around enticing more providers into the profession. But, I think we’re starting to see the pendulum swing. Yes, we need more physicians and advanced practitioners, but the healthcare industry is finally allowing technology to help solve this challenge as well. In no other industry have we seen such a “horse-and-buggy” mindset as we do in healthcare. But, there is light at the end of this long and windy tunnel! If you had told me 10 years ago, when I transitioned to a career in healthcare technology, about things like healthcare Artificial Intelligence and Machine Learning, it would have triggered a blank stare as my mind wandered to episodes of Star Trek: The Next Generation. It would have seemed like a beautiful but impossible dream.
Today, there are companies building tools that predict a bad outcome before it happens. These AI tools provide a risk score and alert the right resource, at the right point of care, before it’s too late. And, the tool isn’t just predicting a “one-size-fits-all” outcome based on the disease using EMR data like labs and vitals. It’s predicting and preventing an adverse outcome for one specific patient by interpreting structured and unstructured EMR data, social data, population analytics, and “truths” about that patient across the continuum of care. If I just let that sink in, it almost drives this “former” doctor to tears. I remember manually carrying tubes of my patients’ blood, spinal fluid, and even less glamorous specimens down a creepy hallway in the bowels of Parkland Memorial Hospital to the lab. I feared that my patients’ tests and the results I desperately needed would vanish. Then, I would spend the next several hours religiously checking for results on the original Macintosh dinosaur that required a whack on the case to stop flickering.
Technologies like cognitive processing can immediately flag a chronic congestive heart failure (CHF) patient with diabetes for a pharmacist consult when he arrives in the E.D. with a broken wrist – because he hasn’t filled his meds in over 8 weeks. The enormity of marrying ambulatory data, social behavior, and inpatient EMR data to preemptively keep this CHF patient from returning with a hypertensive or diabetic crisis is mind-boggling. Some forward-thinking health systems that are using these tools are seeing results like a 31% reduction in readmission rates among CHF patients.
Technologies like machine learning can independently aggregate and interpret new data to reveal hidden insights that humans could never manually process. Not only are these technologies significantly improving the quality of patient care, but they driving enormous cost and efficiency savings by allocating resources in a prioritized manner and reducing costly adverse outcomes. These technologies are enabling personalized surveillance, prediction, action, and reporting for patients – whether they’re at home, in their primary care provider’s office, in the E.D., or even in the ICU.
I spent some of the happiest years of my career scurrying around the ward with my tattered copies of Pharmacopeia and Sanford Guide in hand. Then, the smart phone, loaded with the Epocrates app, arrived. It was life-changing. My colleagues and I were convinced that it couldn’t get any better than that. Technology had peaked. But, fast forward a decade or so (again, no need for exact math), and the disruption of our technology-averse industry continues! I often tell my colleagues who are still practicing to seize the new technologies available to them. After all, embracing innovation is why we are no longer whacking that old, flickering Macintosh.
“Writing in English is like throwing mud at a wall.”
― Joseph Conrad
I recently watched the 2016 movie “Arrival.” The film explores the idea that what you think and how you think may actually be closely intertwined. “Arrival” is a story about humanity’s first contact with aliens and how a pair of scientist find ways to communicate without a common language. As they spend more and more time with the octopus-like creatures, they get increasingly frustrated with their lack of progress and must get creative in order to effectively communicate with these new visitors to Earth. I won’t spoil it for you, but this film beautifully illustrates how powerful and difficult the use of language can be, whether it’s between a linguist and a 10-foot-tall mollusk or with each other.
In a way, Electronic Medical Records (EMRs) can be seen as big repositories of human language about patients. Patient charts are largely written observations, results, and decisions that can be done with pen and paper or, now, with EMRs – completed with a computer keyboard. Depending on the size of the practice or hospital, this means the creation of hundreds or thousands of notes a day, including Progress Notes, Admission Notes, Procedure Notes, Discharge Summaries, Consultation Notes, and more.
These narrative clinical notes are largely entered by providers, nurses, and the rest of the care team essentially writing sentences and paragraphs. These notes are the bread and butter of patient documentation – it’s natural for medical professionals to record what they’re thinking or discovered in the same manner in which they would tell a colleague in conversation. The design of EMRs did not spring fully formed out of thin air. Like most innovations in technology, these systems were designed predominantly as charge capture repositories, but with the intention of making some improvements the day-to-day workflows of doctors, nurses, and other patient care professionals. If paper charts hadn’t existed before the invention of EMRs, they may have been designed differently to better leverage computers and perhaps better organize the intake and recording of patient data. But, for the foreseeable future, narrative documentation isn’t going anywhere.
Humans can obviously read and execute decisions based on individual notes, but the manual time and effort to do so is drastically inefficient. One study found that when hospitalists were reviewing notes, much of the content received little attention or was read very quickly. But, even if providers and care teams could ingest these notes with 100% accuracy and speed, one of the heralded advantages of EMRs has always been that patient data would be digital so computers and their ever evolving algorithms can ingest and interpret them.
As a result, we’ve seen entire new industries spring up and succeed in doing just that, such as via Population Health reporting tools to analyze hundreds of patients at a time, or Clinical Decision Support tools that provide recommendations at the point of care – just to name a few. However, up until recently, the bulk of this kind of data analysis has been limited to the relatively small amount of discrete data (approximately 20%) found in the EMR, which are typically entered in specific, discrete formats, such as the required selection of specific ranges of numbers for vitals or laboratory data fields.
One of the most exciting areas of recent innovation in healthcare technology has been in the field of “Natural Language Processing,” or NLP, which uses cognitive computing algorithms to allow a computer to “read” unstructured text and pick out key words and phrases, in context to “understand” its meaning. This allows computers to tap into the vast, previously unexplored swaths of note data that is simply unreadable by standard tools limited to ingesting only discrete data. Not surprisingly, a vast amount of useful clinical data is found in progress notes, nursing notes, and other free text notes that are not redundantly also documented in discrete fields. And, with Artificial Intelligence, not only can NLP be used to extract written out thoughts and findings about patients, but it can be leveraged to find patterns and run analysis to lead to discoveries that the doctor or nurse didn’t even realize when writing those notes! All this at lightning speed compared to any cost-prohibitive, manual attempts to do this work by hand.
NLP can be used in a variety of applications, such as helping advertisers read social media posts to improve their ad targeting or to help a computer compete in Jeopardy!. And there have even been successes in blending the line between social data and clinical data, such as a study that combined tweets about asthma with data taken from air-quality sensors and EMR data to predict with 75% accuracy if the Parkland emergency department staff could expect a high, low or medium number of asthma-related visits that day. NLP is being used by many young, innovative companies as a powerful tool to help provide real-time, personalized clinical decision support to identify for medical risks like sepsis or COPD, before they occur. This is an emerging and exciting area of study, and the ability and accuracy of NLP combined with machine learning will likely become ever more powerful and beneficial to uncover hidden gems of insight from patient data that was previously unable to be explored.
Yes, language (especially the English language) can be messy, but with the emergence of NLP there’s hope that computers are starting to bridge the gap to a better understanding of human language, which will allow humans to better understand each other and improve patient care.