Predictive analytics and advanced labor management are the most important – and underutilized – methods to assure that provider organizations have the right caregivers in the right places at the right times.
A recent survey of nurse managers by AMN Healthcare and Avantas, Predictive Analytics in Healthcare 2016: Optimizing Nurse Staffing in an Era of Workforce Shortages, (available on the AMN website) brought the need for more awareness to light in just a few stats related to staffing and scheduling:
94 percent of nurse managers say that understaffing hurts staff morale
Nearly 70 percent say they are very concerned about the impact on patient satisfaction
More than 50 percent say they are very concerned about the effect on quality of care
The survey also revealed a lack of sophisticated scheduling tools being utilized:
24 percent use paper-based scheduling and staffing tools
19 percent use simple digital spreadsheets
23 percent don’t use any scheduling tools at all
Further, the survey found that while nearly 90 percent of nurse managers said that a technology that can accurately forecast patient demand and staffing needs would be helpful, 80 percent were unaware that such a solution exists.
Strategies to Fulfill the Potential Predictive Analytics
This process to predict future patient demand and strategically plan clinician scheduling and staffing is scalable, cost effective and accurate. First, staffing data are processed with advanced algorithms, then forecasting models are created and validated, customized for each unit or service area within the organization, allowing workforce projections up to 120 days prior to the shift. The forecast is updated weekly, and by 30 days in advance of the shift, the forecast of staffing need is 97 percent accurate.
Compared to how scheduling and staffing is conducted at most healthcare organizations today, predictive analytics may seem like something out of a sci-fi movie. The truth is, this sophisticated forecasting of labor needs has been leveraged in other industries with great success. And, in healthcare, it can lay the foundation for significant advancement in utilization of staff, leading to improvements in morale, quality, and financial results. The advanced labor management strategies and tools layered on an accurate projection of staffing needs – months and weeks in advance of the shift – will turn an accurate forecast into an effective resource management strategy.
Adopting Workforce Analytics Every organization’s staffing mix should be unique to the fluctuations in its patient volume. Once an organization understands its demand, it can then determine its supply – scheduling and staffing to meet patient demand in the most productive manner possible. The organization can analyze and solve the problems that reduce its available supply of core staff, such as leaves of absence, continuing education, training and other issues. This precision understanding of workforce availability is then layered with patient volume predictions, and the result is accurate insight into the core and contingency staffing levels needed to meet patient demand.
Guest post by Dan Hickman, chief technology officer, ProModel.
With six in 10 U.S. hospitals functioning at operational capacity, patient flow optimization provides one of the most cost-effective ways to increase a hospital’s bottom line.
Around 6 a.m. every day, hospital-wide “huddles” occur to discuss and determine a collective understanding of the state of operations. Most of these huddles take less than an hour and provide hospital and departmental leaders a snapshot of census status and expected discharges.
But hospitals are complex, dynamic systems. By 7 a.m. a flood of patients could hit the ED, affecting everything from staffing to the census, and carefully crafted plans disintegrate.
Consider the current state of patient flow at most hospitals.
Most health systems today have a reactionary approach to admit, transfer and discharge (ADT), patient flow, census, and staffing. Moreover, there is no way of accurately predicting future patient flows to right-size staffing and optimize workflows.
Discharge processes are open loop, resulting in costly delays. Most hospital staff use spreadsheets stating the number of discharges planned for the next 48 hours. However, there is no way to look at patient census with diagnosis codes tied to the typical length of stay.
The current state of patient flow results in multiple problems:
For many hospitals, the length of stay and cost per case metrics exceed CMS value-based care efficiency measures affecting reimbursements and the bottom line.
The daily reality of hospital staff revolves around logistics — the timely and accurate flow of patients coupled with staff, equipment, and facilities needed to accommodate and provide care within the hospital. Yet most hospitals lack the tools to define, visualize, predict and optimize the logistical flow of real-time needs into the near-term future.
Compounding the problem, many patients cite time spent ‘waiting’ as an issue affecting their experience, and ultimately patient satisfaction and the hospital’s HCAHPS scores.
Hospitals are really good at examining what’s happened to a patient in the past. The staff knows where they’ve been, but they haven’t taken the next leap, which other industries have, at projecting out where they think patients will “flow” during their stay and how the next 24 to 48 hours could affect the status and the census. There are parallels with other highly complex industries where accuracy and logistical management are critical to safety and success. One example — air traffic control.
Founded in 2012 by former home health agency owner Dan Hogan, Medalogix has been recognized by Harvard University, HIMSS and Fierce Healthcare IT as an innovative solution that’s improving America’s Healthcare system. Medalogix currently offers two solutions, Touch and Bridge.
Medalogix offers patient outcome management (POM) solutions that use a combination of predictive analytics, workflows and business intelligence engines to improve quality and reduce costs.
We’re a healthcare tech company that provides analytics and workflows to home health providers so they can improve care and reduce costs.
Medalogix currently offers two predictive analytic workflow solutions to home health providers:
Touch, which helps ensure a patient’s successful care episode by identifying patients at risk of transferring off census (i.e. hospital readmission), falling or who would benefit from an additional care episode. After identification, Touch then automates a clinical team’s patient touchpoints to encourage the best care and cost outcome.
Bridge, identifies which patients would benefit from hospice care and then offers a workflow to help clinicians have the right end of life conversation with the right patient at the right time.
Prior to founding Medalogix, Dan Hogan owned and operated a home health agency. As the industry moved toward digital patient records, he realized there was an opportunity to analyze those streamlined data sets to detect health risks the human eye might miss. He researched to find a tool that could do this, and came up empty handed. He decided to take matters into his own hands and incorporated Medalogix in February 2009.
Currently, Medalogix integrates with leading home health and hospice EMR. We market to their users through webinars, industry conferences and referrals through existing satisfied Medalogix users.
RightPatient is a division of M2SYS Technology, an ISO 9001:2008 certified company and biometric technology solution provider. M2SYS has more than a decade of biometric technology experience, with more than 300 million enrolled users in more than 100 countries.
RightPatient is the industry’s most advanced biometric patient identification, patient engagement, personalized healthcare and healthcare intelligence platform to reduce costs and liability, improve quality of care, monitor population health and enhance the patient experience. With features for wearable and biosensor integration, health games, medication alerts, proactive health management and predictive analytics, the platform also integrates with major electronic health record (EHR) systems such as Epic, Siemens, Cerner, McKesson, Meditech, IBM and many others. RightPatient is already deployed across hospitals and health systems that collectively maintain the health data of over 10 million patients.
RightPatient is the industry’s most advanced patient identification, patient engagement, personalized healthcare, and healthcare intelligence platform to reduce costs and liability, improve the quality of care, monitor population health, and enhance the patient experience. Our healthcare ecosystem unifies clinical knowledge through data aggregation, deep learning, and predictive analytics to personalize medicine, improve outcomes, and reduce re-admissions.
Our founder and CEO Mizan Rahman immigrated to the U.S. in the late 1990s seeking to turn some of his ideas, education, and experience into tangible products that solved problems for different verticals. He has successfully shepherded two companies from startup to a multi-million dollar companies that were eventually bought out.
Mizan now oversees the strategic and operational interests of the company worldwide combining his software engineering experience and entrepreneurial leadership with comprehensive international market intelligence to solve customer problems through identity solution ingenuity. He has successfully shepherded the growth of M2SYS as a global force in identity management, pioneering the development and launch of M2SYS’ Bio-Plugin biometric middleware and Hybrid Biometric Platform – both of which were recognized by Frost and Sullivan with prestigious awards for their design innovation.
Mizan continues to be recognized for his innovation and leadership in the field of biometric identification technology, most recently as recipient of “Technology Innovator of the Year” by InfoWorld. He is a frequent speaker in many US and international conferences, symposiums and universities such as the International Biometric Conference and MIT.
To better understand the fundamentals of predictive analytics — and why it has the potential to transform healthcare — it can be helpful to use Netflix as an illustrative example.
Let’s say, for example, you’re sitting around the house one rainy October Saturday and decide to view a few movies using Netflix’s streaming service. First you watched Field of Dreams then you decided, hey, this rain isn’t letting up any time soon, and that dog doesn’t want to go out in it any more than I do. So, after a brief backyard sojourn during which you and the dog confirmed that 38 degrees and rainy is in fact unpleasant — you reconvened your Netflixing and ended up watching Bull Durham, as well. Further, let us also assume that you enjoyed both movies and watched them all the way through.
As the credits rolled on Bull Durham, the critical question for Netflix was the same it always is: What would you enjoy watching next — specifically, what should Netflix recommend? Based upon the day’s viewing you may have a soft spot for baseball movies. Though it could just as easily be the case that you’re Kevin Costner’s biggest fan and the fact that you queued up two of his baseball movies was pure coincidence.
Given the uncertainty orbiting these pieces of information, maybe the best prediction would be Dances with Wolves, starring Kevin Costner. Or maybe the right pick would be Moneyball, the story of how the Oakland A’s leveraged data-driven, evidence-based sabermetrics to remain competitive against much more highly capitalized MLB teams. But what if neither Kevin Costner nor baseball is the most important correlation—what if the best predictor of whether you’ll like a film is simply whether it’s a sports movie from the late 80s?
As with all forms of predictive analytics, the question of what to recommend multiplies in complexity as overlapping variables (often in the form of unstructured data) are added and subsequently considered within algorithmic equations that power, in this case, Netflix’s recommendation engine. Further complicating the matter, it’s likely that you’re not the only person to have watched both of these films in close proximity and there are likely to be numerous “motivations” for such viewings across the population. It becomes apparent rather quickly the inherent challenge of something that seems, on the surface, as straightforward as a recommendation engine.
In healthcare we face these same kinds of challenges, just in a different form. The questions we ask are which gaps in care create the greatest risk for the patient, or which specific combinations of gaps in care correlate with readmissions—so that clinical outreach coordinators and other staff can prioritize whom to contact right away. We ask which types of claims are most likely to be under-coded or missing charges—so that organizations can make best use of finite resources like staff time and ensure the greatest positive impact on overall financial performance.
Hospital readmissions continue to be a major contributor to soaring healthcare costs and a drain on the U.S. economy. According to the Robert Wood Johnson Foundation, 4.4 million hospital readmissions account for $30 billion every year, while 20 percent of Medicare patients are expected to return to the hospital within 30 days of discharge. The Affordable Care Act of 2010 requires the U.S. Department of Health & Human Services to establish a readmission reduction program.
This program provides incentives for hospitals to implement strategies to reduce the number of costly and unnecessary hospital readmissions. Centers for Medicare and Medicaid Services (CMS) has created quality programs that reward healthcare providers and hospitals with incentive payments for using electronic health records (EHR) to promote improved care quality and better care coordination. The reasons for hospital readmissions include adverse drug effects (ADE), lack of a proper follow-up care, inability of patients to understand the importance of their medications and diagnoses, unidentified root causes, and misdiagnosis. Technology could play a vital role here by properly documenting, tracking, diagnosing, monitoring, and enabling better communication between patient and provider.
Adverse drug events constitute the majority of hospital readmissions. A cohort study, including a survey of patients and a chart review, at four adult primary care practices in Boston (two hospital-based and two community-based), involving a total of 1202 outpatients indicated that 27.6 percent of these ADEs were preventable, of which 38 percent were serious or fatal. Human error was the leading contributor to these ADEs, followed by patient adherence. Additionally, patients who screened positive for depression were three times as likely to be readmitted compared to others.
Our analysis indicates that 28 percent of adult hospital stays involved a mental health condition. A study of Medicaid beneficiaries in New York State determined that, among patients at high risk of rehospitalization, 69 percent had a history of mental illness and 54 percent had a history of both mental illness and alcohol and substance use. We know that a properly implemented mental health screening protocol can lead to effective diagnosis, and that proper management of these issues can positively impact the reduction of hospital readmissions.
Further studies show that most cases of readmissions for certain chronic conditions have an underlying mental health issue, which appears in patients who have not been previously diagnosed for a mental health condition (i.e., anxiety, bipolar disorder or depression). For example, anxiety and/or depression increases the risk of stroke and decreases post-stroke survival, and plays a key role in diabetes treatment as 33 percent of this patient population is found to be depressed and patients with bipolar disorder have reduced life spans. Other cases where depression affects the patient’s survival and treatment cost include hypertension, stable coronary disorder, ischemia, unstable angina, post myocardial infarction and congestive heart failure.
An important point to note: congestive heart failure is the major driver of hospital readmissions in the U.S., accounting for 24.7 percent of all readmissions. Another study concluded that patients with severe anxiety had a threefold risk of cardiac-related readmission, compared to those without anxiety.
Guest post by Anil Jain, MD, FACP, chief medical officer, Explorys, and staff, Department of Internal Medicine, Cleveland Clinic.
Nearly every aspect of our lives has been touched by advances in information technology, from searching to shopping and from calling to computing. Given the significant economic implications of spending 18 percent of our GDP, and the lack of a proportional impact on quality, there has been a concerted effort to promote the use of health information technology to drive better care at a lower cost. As part of the 2009 American Reinvestment and Recovery Act (ARRA), the Health Information Technology for Economic and Clinical Health (HITECH) Act incentivized the acquisition and adoption of the “meaningful use” of health IT.
Even prior to the HITECH Act, patient care had been profoundly impacted by the use of health informationtechnology. Over the last decade we had seen significant adoption of electronic health records (EHRs), use of patient portals, creation of clinical data repositories and deployment of population health management (PHM) platforms — this has been accelerated even more over the last several years. These health IT tools have given rise to an environment in which providers, researchers, patients and policy experts are empowered for the first time to make clinically enabled data-driven decisions that not only at the population level but also at the individual person level. Not only did the 2010 Affordable Care Act (ACA) reform insurance, but it also has created incentive structures for payment reform models for participating health systems. The ability to assume risk on reimbursement requires leveraging clinical and claims data to understand the characteristics and needs of the contracted population. With this gradual shift of risk moving from health plans and payers to the provider, the need to empower providers with health IT tools is even more critical.
Many companies such as Explorys, a big data health analytics company spun-out from the Cleveland Clinic in 2009, experienced significant growth because of the need to be able to integrate, aggregate and analyze large amounts of information to make the right decision for the right patient at the right time. While EHRs are the workflow tool of choice at the point-of-care, an organization assuming both the clinical and financial risk for their patients/members needs a platform that can aggregate data from disparate sources. The growth of value-based care arrangements is increasing at a staggering rate – many organizations estimate that by 2017, approximately 15 percent to 20 percent of their patients will be in some form of risk-sharing arrangement, such as an Accountable Care Organization (ACO). Already today, there are currently several hundred commercial and Medicare-based ACOs across the U.S.
There is no doubt that there are operational efficiencies gained in a data-driven health system, such as better documentation, streamlined coding, less manual charting, scheduling and billing, etc. But the advantages of having data exhaust from health IT systems when done with the patient in mind extend to clinical improvements with care as well. We know that data-focused health IT is a necessary component of the “triple-aim.” Coined by Dr. Donald Berwick, former administrator of the Centers for Medicare and Medicaid Services (CMS), the “triple-aim” consists of the following goals: 1) improving health and wellness of the individual; 2) improving the health and wellness of the population and 3) reducing the per-capita health care cost. To achieve these noble objectives providers need to use evidence-based guidelines to do the right thing for the right patient and the right time; provide transparency to reduce unnecessary or wasteful care across patients; provide predictive analytics to prospectively identify patients from the population that need additional resources and finally, use the big data to inform and enhance net new knowledge discovery.
Over the past year, economic pressure and regulatory changes have increased scrutiny around areas of inefficiency within the healthcare industry. With new policies like the Affordable Care Act creating the need to improve patient outcomes and prevention, 2014 will be the year for much needed efficiency upgrades across the board at hospitals. And with mounting pressure to cut costs amidst anticipated physician and other major shortages, new and innovative ways to leverage technology will be called upon to usher in changes for the healthcare industry.
The business of care will continue to be a major area of focus for hospitals in 2014. Preventable, adverse events because of medical errors cost the healthcare industry more than $29 billion in 2013 and have led to between 50,000 to 100,000 deaths each year. Healthcare professionals and hospitals cannot afford to continue accepting medical errors as balance sheet losses, which are not only jeopardizing profitability, but patient care. To save money and improve patient care at the same time, hospitals will look to learn from technology being used successfully by other industries in 2014 to enhance real-time analysis and, thereby, prevention and outcomes.
Guest post by Harry Jordan, vice president and general manager, healthcare for LexisNexis.
The most important question in identity management is not: “Who are you?” It’s “What do we need to know about you?” And nowhere is the answer to that question more critical than in healthcare, where inadequate systems and processes can not only threaten business integrity and success, but jeopardize lives, as well. Inevitably, it is time to shift the focus of the discussion of identity management away from authentication methodology and toward the broader healthcare context in which identity management is no longer a luxury, but a necessity.
Effective patient/member identity management springs from this fundamental question: “Given what we are trying to accomplish through this particular transaction, what do we need to know about this individual to insure safety, integrity and trust?” Or, more elaborately: “What do we need to know to prove this individual is who they say they are and that they are authorized to access the information being requested based on those identity credentials?”
The answer is determined by the intersection of multiple factors: your objectives; product and service characteristics; population demographics and attitudes; the nature, value and riskiness of the transaction being performed; the point in the process and relationship where it takes place; and organizational risk tolerance. Getting the answer right is critical to the sustainability of health care organizations and, more importantly, the safety of the individuals they serve.
Identity fraud is the fastest growing crime in the United States, affecting more than 11 million adults in 2010. Medical identity fraud is the fastest growing type of identity theft. The Ponemon Institute estimates the annual economic impact of medical identity theft to be nearly $31 billion.
Health care consumers will, and should, expect their data to be secure at all times in order to protect their financial and physical well-being. Health care stakeholders will demand solutions that ensure they are dealing with the right person, at the right time, for the right transaction, thereby minimizing risk and negative impact on their health care delivery decisions, the health of their patients and overall business performance.
As a recent Gartner report states, identity management is “increasingly recognized as delivering real-world business value,” and “identity management agility improves support for new business initiatives and contributes significantly to profitability.” Identity management is rapidly evolving to encompass emerging risks and application variability. There are tools you can put in place now to meet the increasing demands of identity management.
Point solutions and one-size-fits-all implementations are being supplanted by or absorbed into more comprehensive and flexible approaches. These solutions provide identity management coherency across processes and relationships, as well as identity management consistency across multiple channels and organizations.
At the same time, they enable organizations to efficiently implement a wide range of identity management tools that blend the right identity elements together with the appropriate view and assurance level for each transaction. Established organizations can layer new identity management capabilities onto existing systems in the form of services. Merely extending enterprise identity management solutions will not work.
Three key concepts are at the core of the most successful health care consumer identity management solutions. They are general principles shared by diverse business-specific implementations.
1. Identity management is as much about business as about security. Identity validation (or “resolution”), verification and authentication – commonly regarded as security functions – have far-reaching business ramifications. How you perform them can strongly shape your most direct and therefore vital interactions with patients, payers, providers and other healthcare stakeholders. Thus, while it is important, and sometimes mandatory, to follow industry standards, it is also critical to make sure that the way in which you implement identity management is tailored to your market, business plan and mission to maximize business goals and minimize organizational risk.
2. “Know your health care consumer” is the point of balance for multiple – and possibly competing – objectives. “Know your healthcare consumer” is a phrase that traditionally has different meanings to health care consumer service than it does for security management Service people are concerned with raising healthcare consumer satisfaction by increasing access and ease. Security people are concerned with reducing risk by restricting access.
3. Ask for only what you need to know. Knowing more can, in fact, enable you to ask for less information. In identity management industry jargon, the objective is “friction reduction” through “data minimization.” Improve the health care consumer experience by not asking for information you don’t need.
Strong security can be, for the most part, invisible to the user. Analytics operating in the background can spot links between healthcare consumer data and suspicious entities or recognize suspicious patterns of verification failure.
Analytics can be integrated with business rules to adjust the security level and trigger appropriate treatments or approval of treatments. They can also be used to determine if the current transactional pattern of behavior is unusual. Reacting to healthcare consumer responses in real time – taking business rules for different product lines, channels and types of transactions, and an entity’s tolerance for risk – an identity management service can make dynamic decisions about when to invoke additional and/or stronger measures.
The number of identity-reliant transactions engaged in across the health care continuum is multiplying rapidly and becoming ever more critical to the success of individual health care organizations. When dealing with any situation involving the sharing of a patient’s personal health information it is essential these organizations ask themselves the fundamental question about the individual or entity with which they will be sharing the information: “What do we need to know about you?”
This question is the starting place for all other questions in identity management. The right answer is the key to making identity management an enabler of great services accessed with ease and delivered at a low coast and minimal risk of fraud.
Harry Jordan is Vice President and General Manager, Healthcare for the risk solutions business of LexisNexis. He directs the healthcare business, offering capabilities in health management, predictive claims fraud analytics and health information exchanges.
Healthcare big data is a big story, and it’s only going to continue being one. It’s a story I like and am intrigued by, but it’s not very sexy. Because of this, the only pieces of information about it seems to be very technical.
Until we actually see how big data changes lives, there’s just not going to be warm and fuzzy stories about it. So, cold and technical it is; nonetheless, I’m still fascinated.
In searching information about the subject, because I too want to know more from a ground floor level, it was nice to come across a nice piece about big data on the Cleveland Clinic’s website.
So, getting right into it, here’s an interesting piece of trivia about healthcare big data directly from the Clinic: “The amount of data collected each day dwarfs human comprehension and even brings most computing programs to a quick standstill. It is estimated that 2.5 quintillion bytes of data are created daily, so much that 90 percent of the data in the world has been created in the last two years.”
Healthcare big data is essentially large amounts of data that’s difficult to manipulate using standard, typical databases. Essentially, big data is very large pieces of information that ultimately, when captured can analyzed, dissected and used to monitor segments within a given sect.
Healthcare big data, it is thought, is what will drive change in care outcomes. What’s interesting, though, is that even though there’s a tremendous amount of data available for use, it’s just not being collected in a structured manner.
Collecting structured data is a must if we are going to begin putting some muscle to the bone of the new healthcare ecosphere we’re putting in place. You don’t have to take my word for it; IDC Health Insights research director Judy Hanover spoke of the same subject recently here.
But, to prove my position, I’ll let Cleveland Clinic make the point: “Unfortunately, not enough of this deluge of big data sets has been systematically collected and stored, and therefore this valuable information has not been aggregated, analyzed or made available in a format to be readily accessed to improve healthcare.”
Also according to the Clinic, if all of the data currently available were used and analyzed, it would be worth about $300 billion a year, reducing “healthcare expenditures by almost 8 percent.”
At the heart of healthcare big data is the hope that it can eventually help providers become predictors. Essentially, big data is like a big crystal ball, or so it’s been said.
According to Cleveland Clinic: “In this way, analytics can be applied to better hospital operations, track outcomes for clinical and surgical procedures, including length of stay, re-admission rates, infection rates, mortality, and co-morbidity prevention. It can also be used to benchmark effectiveness-to-cost models.”
Predictive analytics: That’s what it’s all about.
With all of the attention being given big data and warnings about being prepared for big data so it doesn’t sneak up on you – like meaningful use and ICD-10 – are valid and should be taken seriously.
Efforts are currently underway and available for big data processing and by managing data, “This dynamic data management technology makes data analysis more efficient and useful. Access to these data can also significantly shorten the time needed to track patterns of care and outcomes, and generate new knowledge. By leveraging this knowledge, leaders can dramatically improve safety, research, quality, and cost efficiency, all of which are critical factors necessary to facilitate healthcare reform,” writes Cleveland Clinic.
Big data is a catalyst for change, and without sounding caustic, will be a bigger deal than electronic health records currently are. Without a commitment to it, practices and healthcare systems will be left behind.