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.
Nadia Christensen, MD
Unfortunately, it happens all the time. You take your elderly father into the hospital for a routine surgery such as a total hip replacement, and while being treated for the hip, he ends up with a Hospital-Acquired Condition (HAC). Think of an HAC as your bothersome Aunt Ethel, an unwanted house guest who shows up when you least expect her, bringing in suitcases full of complications, and leaving a trail of havoc in her wake. Simply stated, an HAC is a potentially avoidable infection or complication that occurs while a patient is hospitalized for an unrelated condition. Years ago, people referred to these conditions as Healthcare-Associated Infections (HAIs) or nosocomial infections. However, today many preventable conditions, which are not infections such as pressure ulcers or DVTs, have been added to the list and term broadened to HACs.
Trends in HAIs and HACs before HACRP
In 2013 the AHRQ showed a 17% decrease in HACs. This reduction was linked to a prevention of 50,000 deaths and a cost savings of $12 billion dollars. Despite this encouraging trend, an estimated 10% of hospitalized patients were still experiencing one or more HACs.
On the infection front, in 2014 the CDC estimated that on any given day, 1 in 25 hospitalized patients would develop at least one HAI. Although this number had seemed very high, the CDC, in its annual National and State Healthcare-Associated Infections Progress Report, found that rates of HAIs are actually decreasing. There was a 50% decrease in central line-associated bloodstream infection and a 17% decrease in surgical site infection after abdominal hysterectomy. Furthermore, there was a 13% decrease in hospital-onset MRSA blood infection and an 8% decrease in C. difficile infection.
Although the trends in HAIs and HACs were going in the right direction, from a legislative perspective, efforts were made to further decrease HAC-related mortality, morbidity, and cost. The Hospital-Acquired Condition Reduction Program (HACRP) beginning in fiscal year 2015 required CMS to reduce payments to hospitals who are in the lowest 25% of all hospitals in HAC performance score.
HACs Measured & Hospitals Participating
HACRP doesn’t measure all potential HACs. It measures a subgroup of HACs divided into two domains:
Domain 1 – AHRQ Patient Safety Indicator (PSI) 90 Composite (includes pressure ulcers, pneumothorax, CVCBSI, hip fracture, PE/DVT, postop sepsis, wound dehiscence, accidental puncture/laceration)
Domain 2 –NHSN Healthcare-Associated Infection (HAI) measures:
- Central Line-Associated Bloodstream Infection (CLABSI)
- Catheter-Associated Urinary Tract Infection (CAUTI)
- Surgical Site Infection (SSI) – colon and hysterectomy
- Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia
- Clostridium difficile Infection (CDI)
The two domains are measured over a two-year time period, followed by a brief period of review and correction, and the payment adjustment is applied to the following fiscal year. The time periods for the two domains are staggered – so, in essence, the measurement occurs over a 2.5-year period.
For example: for 2017, HACs were measured from 2012-2015, data review occurred in 2016, and the payment adjustment will be applied to this coming fiscal year.
For 2017, 3023 hospitals are included in HACRP. The program includes acute care hospitals but excludes long-term care hospitals, critical access hospitals, rehabilitation hospitals, psychiatric hospitals, children’s hospitals, and IPPS exempt cancer hospitals, and religious nonmedical health care institutions. In Guam, US Virgin Islands, Northern Mariana Islands, and American Samoa, it also excludes short term acute care hospitals.
Even Hospitals Dread Report Card Day
The payment adjustment for the HACRP is a payment penalty. There is no financial incentive opportunity. No matter how much they study for the HACR “exam”, there’s no extra credit! To calculate if a hospital will be subject to payment reduction, each of the above listed measures is assigned a score between 1 and 10 based on the hospital’s performance – with higher scores indicating worse performance. Note, if a hospital does not have sufficient data for a given measure, the measure is excluded from the calculation. Also, the measures in the two domains are not weighted equally. For 2017, Domain 1 has a 15% weight and Domain 2 has an 85% weight in the hospital’s total score.
This program “grades” hospitals on a curve. Just like the old classroom days, hospitals are competing against each other on a yearly basis. As rates of HACs decrease across all hospitals, hospitals must work even harder to improve and keep their score in the top 75%. For FY 2017, hospitals with a total HAC score greater than 6.5700 are subject to the 1% payment reduction. But, talk about a painful report card “F”! The payment reduction is applied to all the hospital’s CMS payments for the year.
HAC Penalties for 2017
CMS states that 769 of 3,203 hospitals ranked in the worst performing quartile will be subject to the 1% penalty in 2017. This is compared to 751 hospitals out of 3,211 hospitals penalized in 2016.
HACRP, with its huge penalties totaling $364M last year, is coming under scrutiny in the year end, as clinicians question the validity of their calculations. In a recently published study, data shows that large hospitals are more likely to be identified as poor performers for measures that have a very low probability of complications. Additionally, prior studies have found there is a surveillance bias for more vigilant hospitals looking for complications. No good deed goes unpunished, they say.
The bottom line
There is an overabundance of programs available to help hospitals reduce HACs. On a national level, the CDC itself provides a variety of assessment tools, strategies, toolkits, and checklists that a hospital may implement. On state level, many evidence-based initiatives are available to aid in reduction of HACs. As hospitals work on many of these programs, they struggle to identify the interventions will help them pull ahead of the curve and keep them in the top 75% of hospitals.
In this new era of medicine, tools are available to hospitals that, a decade ago, clinicians would have described as figments of a sci-fi lover’s imagination. Technologies built on cognitive computing are now available to provide real time detection of HACs before they happen. Imagine a piece of technology “smart” enough to know that an upward trending white blood cell count in a post-operative patient with a urinary catheter should be brought to a physician’s attention. Getting this information to the right clinical team member, before the patient develops a urinary catheter-associated infection, is truly turning the traditionally reactive practice of medicine into a proactive one!
Early identification and detection of patients needing closer monitoring and/or interventions will not only improve the quality of patient care, lower mortality, decrease LOS, but will also help hospitals keep their hard-earned CMS dollars in the bank. And more importantly, it keeps those pesky, luggage-toting unwanted visitors out!
Nadia Christensen, MD
Hospital readmissions account for a huge portion of US healthcare costs. As clinicians we are always trying to understand the root cause of why one of our patients might bounce back. In some circles, we refer to this as the boomerang effect.
Over the years, in an effort to stem the rising costs of healthcare dollars spent on readmissions, CMS has created many programs to encourage hospitals to address this problem. One of these programs is the Hospital Readmission Reduction Program (HRRP). HRRP was established by the Affordable Care Act in 2012 and required CMS to reduce payments to hospitals with excess readmissions.
A readmission is simply defined as a repeat admission to a hospital within 30 days of the discharge date of the original hospital admission. This readmission may be at the same or different hospital, and it may be for any cause which may or may not be related to the condition for which the patient was first admitted.
In order to better understand why CMS created this program, it might be advantageous to take a few moments to consider the landscape of readmissions in 2011 – the year before this program was created. In 2011, $41.3B was spent on hospital readmissions for 3.3M patients. CMS, at that time, looked back at historical data for 2004-2009 and found no significant change in 30-day hospital readmission rates. The 2011 heart failure readmission rate was 24.5% , with the price tag of $1.747B. Close behind heart failure was pneumonia, with a readmission rate of 17.9% and $1.148B in costs.
Readmissions Measured & Applicable Hospitals
HRRP measures readmission rates not for all Medicare patients, rather a specific subset of Medicare “fee-for-service” patients admitted to an applicable hospital with a principal discharge diagnosis for one of the following conditions below.
Effective program year 2013:
- Acute myocardial infarction (AMI)
- Heart failure (HF)
Effective program year 2015:
- Chronic obstructive pulmonary disease (COPD)
- Elective primary total hip/total knee arthroplasty (THA/TKA)
Effective program year 2017:
- Coronary Artery Bypass Graft (CABG) surgery
Not all hospitals are included in the HRRP. The program excludes long-term care hospitals, critical access hospitals, rehabilitation hospitals, psychiatric hospitals, children’s hospitals, and IPPS exempt cancer hospitals. In total, approximately 1400 hospitals are excluded from the program.
Readmission Measurement Period
Readmission measurement in this program follows a specific timeline. Readmission rates are measured for a period of 3 years, followed by a brief period of review, and calculation and application of payment adjustment in the following calendar year. For example, for calendar year 2017, readmission rates were measured from 2012-2015, data review occurred in 2016, and the readmission adjustment factors were applied to payments in calendar year 2017.
Calculating Your Hospitals Payment Adjustment
To calculate if your hospital will be subject to payment adjustment in the HRRP, Medicare calculates Excess Readmission Ratio’s (ERRs). ERRs are simply a ratio of your hospital’s adjusted actual readmissions to your hospital’s expected readmissions.
If a hospital performs better than an average hospital that admitted similar patients (with similar risk factors & comorbidities), there is no adjustment to hospital payments. If a hospital performs worse than an average hospital, they can experience as much as a 3% reduction in hospital payments in 2017.
The 3% reduction in hospital payments are not just applied to the 6 conditions for which readmissions are measured. This decrease in hospital payments is applied to ALL your hospitals payments for Medicare for the calendar year!
Readmission Penalties for 2017
Readmission penalties for 2017 are a little frightening! 2,597 hospitals will be facing readmission penalties and the average penalty will increase by a fifth! Medicare expects the penalties will total $528M which is $108M more than last year.
Strategies to Reduce Readmissions
As hospitals consider how to reduce their readmission rates, frequently they will be implementing the many proven interventions known to decrease readmissions. Interventions aimed at care transitions represent one group and include (1) hospital discharge process (2) early post discharge follow-up (3) home care visits during the immediate post-hospitalization period, (4) nurse-led care transition, and (5) remote monitoring strategies.
A second group of interventions aims to reduce readmissions by bolstering patient education and self-management support. A third approach is to use a multidisciplinary team management approach.
The Bottom Line
When considering how many hospitals that already implement these strategies are being penalized in the HRRP for 2017, one might wonder what we may be missing. Are we educating our patients at a level that is higher than their understanding? Are we challenged by language or cultural barriers? Do patients miss follow-up appointments because they have no means to get to the hospital? Are there are financial or mobility barriers that prevent patients from getting to their pharmacy? Do they not have adequate social support? What other social determinants might impede their ability to follow their health plan and keep them from deteriorating and needing a readmission?
As clinicians, we often wonder why the same readmission-prevention interventions work for most patients, yet there are others that defy the odds and bounce back. More and more studies indicate that the “one size fits all’ approach is no longer adequate. Each patient has unique health conditions that impact their ability to successfully transition home and stay well after a hospitalization. Additionally, each patient has unique social, psychological, and cultural barriers that factor into their health outcomes. Forward thinking hospitals are already addressing these factors to significantly reduce the boomerang effect. Programs like the HRRP are a step in the right direction, but the new era of medicine will require more personalized interventions to significantly reduce readmissions.
Nadia Christensen, MD
Hospital margins are narrower than ever, and hospitals dedicate many resources to understanding and capturing their reimbursement dollars. Medicare is the single largest purchaser of healthcare in the US – spending $610B in 2014, which is almost a quarter of all spending on medical goods and services. With inpatient hospital reimbursement being one of the largest pieces of the pie, accounting for 23% of all Medicare spending, sharing how Medicare regulations affect your hospital reimbursement with your staff is paramount in protecting your nest egg of reimbursement dollars. This is the first in a series of blogs simplifying complex CMS reimbursement programs.
When talking about hospital reimbursement, we often have to pull out a white board to make lists and diagrams to follow the complex payment pieces. The Inpatient Prospective Payment System (IPPS) is the first chapter in our “CMS Dollars” story. Put simply, the IPPS program is what determines CMS payment to hospitals. Hospital reimbursement includes:
- An Adjusted Base Pay per patient (per discharge)
- Add-on Payments
- Modifications to Adjusted Base Pay
- Yearly Payment Adjustments
Adjusted Base Pay
The adjusted base pay for a patient is determined by the cost of treating the patient and the market cost of running a hospital in a given location.
In determining the cost of treating a patient’s clinical condition, as patients are discharged from the hospital, their case is categorized into a diagnosis-related group (DRG). As most of us are familiar, DRGs are based on the patient’s clinical condition/procedure and severity of illness. Typically, there are 3 variations of a DRG for the same condition based on absence, presence, and severity of complications.
Weighted DRG. Medicare assigns a “weight” to each DRG that factors in hospital Length of Stay (LOS), resources needed to treat a patient with the given condition severity, additional secondary diagnoses, and patient demographics. The “weighted DRG” is then multiplied by the hospital base pay to calculate the “adjusted base pay”.
Base Pay. The base pay for a given hospital is determined by its geographic location and how this location affects cost of labor and cost of living. When hospital base pay is adjusted for the cost of its labor force, hospitals in more expensive labor markets receive a higher base pay. The second adjustment to base pay, which only applies to hospitals in Hawaii and Alaska, accounts for the higher cost of living in these two states.
Additional payments may be paid to hospitals for the following circumstances:
- Disproportionate share hospital (DSH) payment: For hospitals treating a high percentage of low-income patients
- Indirect medical education (IME) payment: For approved teaching hospitals teaching medical residents
- High-cost outlier payment: Added for cases that are extraordinarily costly
- New technology payment: Added for cases involving certain approved new technologies
Modifications to Adjusted Base Pay
A hospitals payment may also be modified based on different CMS programs. For example, a hospital may see a reduction in payments if their readmission rates are greater than the national average in the Hospital Readmission Reduction Program or if their patients experience more Hospital-Acquired Conditions. Programs such as Value-Based Purchasing may further increase or decrease hospital payments depending on how the hospital is scored in this program.
Yearly Payment Adjustments
On an annual basis, CMS updates its payment rates. This yearly payment adjustment is known as the hospital “market basket” update. For 2017, the increase in the operating payment rates is 0.95%. CMS anticipates that the rate increase, together with other payment policy changes, will increase the market basket up to 1%. However, not all hospitals will see this increase in payment.
In a continuous effort to move hospitals toward meaningful use of EHR and participation in Inpatient Quality Reporting program (IQR), CMS will be withholding a portion or all of the payment increase from hospitals not participating in these two programs.
- Non-compliance with meaningful EHR use will cost hospitals ¾ of the payment adjustment
- Noncompliance with IQR will result in the loss of ¼ of the payment adjustment
- Non-compliance with both programs will result in no adjustment in 2017 payments
Although in 2016 one would expect all hospitals to be compliant with EHR meaningful use and IQR, this doesn’t seem to be the case for small percentage of acute care hospitals.
Current and expected penalties:
- 178 hospitals – 2016 # hospitals penalized for EHR MU non-compliance
- 55 hospitals – 2016 # hospitals penalized for IQR non-compliance
- 179 hospitals 2017 # hospitals penalized for EHR MU non-compliance
- 133 hospitals – 2017 # hospitals penalized for IQR non-compliance
The Bottom Line
For 2017, CMS projects that total Medicare spending on inpatient hospital services will increase by $746 million. The hospitals not participating in the programs above will be leaving a lot of money on the table as Medicare spending continues to grow. Wading through the hundreds of pages of proposed and final CMS rules can be daunting, but the take away is that in 2017, with the market basket update alone, there is an opportunity for hospitals to earn up to 1% additional payment. To put this in perspective, for a hospital who received $400M in CMS payments, a 1% increase in payment ($4M) would pay for a lot of quality and growth initiatives. To claim these dollars, hospitals need to spread a protective wing over their yearly payment adjustment nest egg.