U.S. Department of Health and Human Services (HHS) Secretary Alex Azar and Centers for Medicare & Medicaid Services (CMS) Administrator Seema Verma announce the CMS Primary Cares Initiative, a new set of payment models that will transform primary care to deliver better value for patients throughout the healthcare system. The CMS Primary Cares Initiative will aim to reduce administrative burdens and empower primary care providers to spend more time caring for patients while reducing overall healthcare costs, HHS said in a statement.
“For years, policymakers have talked about building an American healthcare system that focuses on primary care, pays for value, and places the patient at the center. These new models represent the biggest step ever taken toward that vision,” said HHS Secretary Alex Azar. “Building on the experience of previous models and ideas of past administrations, these models will test out paying for health and outcomes rather than procedures on a much larger scale than ever before. These models can serve as an inflection point for value-based transformation of our healthcare system, and American patients and providers will be the first ones to benefit.”
Empirical evidence shows that strengthening primary care is associated with higher quality, better outcomes, and lower costs within and across major population subgroups. Despite this evidence, primary care spending accounts for a small portion of total cost of care, and is even lower for patients with complex, chronic conditions, HHS said.
CMS’s experience with innovative models, programs and demonstrations to date have shown that when incentives for primary care clinicians are aligned to reward the provision of high-value care, the quality and cost effectiveness of patient care improves, the organization cited.
“As we seek to unleash innovation in our health care system, we recognize that the road to value must have as many lanes as possible,” said CMS Administrator Seema Verma. “Our Primary Cares Initiative is designed to give clinicians different options that advance our goal to deliver better care at a lower cost while allowing clinicians to focus on what they do best: treating patients.”
Administered through the CMS Innovation Center, the CMS Primary Cares Initiative will provide primary care practices and other providers with five new payment model options under two paths:
Primary Care First and Direct Contracting.
The five payment model options are:
Primary Care First (PCF)
Primary Care First – High Need Populations
Direct Contracting – Global
Direct Contracting – Professional
Direct Contracting – Geographic
The Primary Care First (PCF) payment model options will test whether financial risk and performance based payments that reward primary care practitioners and other clinicians for easily understood, actionable outcomes will reduce total Medicare expenditures, preserve or enhance quality of care, and improve patient health outcomes. PCF will provide payment to practices through a simplified total monthly payment that allows clinicians to focus on caring for patients rather than their revenue cycle. PCF also includes a payment model option that provides higher payments to practices that specialize in care for high need patients, including those with complex, chronic needs and seriously ill populations (SIP).
Both models under PCF incentivize providers to reduce hospital utilization and total cost of care by potentially significantly rewarding them through performance-based payment adjustments based on their performance. These models seek to improve quality of care, specifically patients’ experiences of care and key outcome-based clinical quality measures, which may include controlling high blood pressure, managing diabetes mellitus and screening for colorectal cancer. PCF will be tested for five years and is scheduled to begin in January 2020. A second application round is also planned for participants starting in January 2021.
Now more than ever, the healthcare industry is leveraging new technologies to provide patients with improved, innovative care. The innovation attracting the most buzz in the healthcare industry today is artificial intelligence (AI). However, despite the ongoing hype of robots and algorithms as industry game-changers, results to date from early applications of AI in healthcare have fallen short of realizing dreams of sweeping improvements.
IBM’s Watson is an excellent example of how these improvements “in healthcare” will require a more step-by-step approach and may take longer to achieve than initially thought. In 2011, Watson garnered worldwide attention by winning a game of Jeopardy against two of the show’s greatest champions. Within healthcare, Watson’s win gave rise to hope that AI was on the precipice of full-scale deployment that would transform the industry and dramatically improve patient outcomes.
For several reasons, that hasn’t quite happened yet, and Watson has found it challenging to deliver improved patient outcomes. While those critical of AI have been quick to jump on these struggles, it’s crucial to acknowledge that Watson suffers from several common obstacles faced by AI in healthcare. These include the lack of high-quality data that can be used to train an algorithm, the low number of available training cases, implicit bias, and the differences in guidelines between the U.S. and other countries.
However, as the industry collectively works to address these issues, I envision three major areas where AI will soon transform personalized medicine.
Individualizing the patient-clinician relationship
Clinicians are already equipping themselves to better serve their patients with the predictive and organizational benefits of AI. This technology will move the field away from a “one-size fits all” approach and make the clinician-patient relationship more individualized, fostering trust.
This would be no small feat for improving the patient-clinician relationship, especially for those suffering from chronic conditions. A study by West Corporation in 2018 found that only 12 percent of chronic condition patients feel strongly that their provider is doing a good job of delivering information specific to their needs and condition.
When a clinician provides patients with unique, individualized solutions, patients feel empowered and are more comfortable speaking up throughout the treatment process. When a patient is comfortable enough to report symptoms, no matter how trivial they may seem, personalized medicine thrives.
With the help of AI, clinicians can search extensive amounts of information to find the causes of patient-reported symptoms and alter patient care accordingly. These improvements can be referenced by other clinicians and lead to large-scale medical breakthroughs.
By Rick Halton, vice president of marketing and product, Lumeon.
For the past decade, EHR investments have been touted as the key to unlocking a transformative, cost-effective, and efficient healthcare industry. A recent study found that spending on EHR systems will continue to dominate healthcare’s technology spend in 2019. But if budgets continue to be prioritized towards optimizing EHR systems, why are there still so many issues related to delivering coordinated care? EHR vendors often do not clearly explain that new issues can arise after implementation, and even make certain processes more complex.
Despite significant investment of EHR systems over the years, care processes continue to be inconsistent and labor intensive. Not only does this result in overwhelming operational costs for hospitals, but it also leads to massive variance in outcomes.
EHR investments are important, but they aren’t a silver bullet. EHRs can only go so far towards improving care outcomes and operations, as they do not address the true problem: disjointed care process issues. Hospitals must consider the broader context that EHRs play into, including investing in greater orchestration and automation of patient care.
By directing investments toward automated digital care plans that are supported by EHRs, hospitals can more effectively connect patients along their entire care journey, and only engage the care team when necessary. Just as the airline industry found success with their equivalent, the “flight plan,” the healthcare industry must provide its own “care traffic control” to deliver coordinated care. This approach is increasingly recognized as care pathway management (CPM).
Opting for “care traffic control?”
The airline industry has successfully crafted and fine-tuned the entire digital trip experience for passengers, which the healthcare industry can utilize in its own way. For example, airline passengers can find out real-time flight status, receive automated updates about seat availability, find information on airports, and be sent data on flight delays.
Both boarding and takeoff are efficient and seamless procedures, with airlines connecting preflight checklists to central airline and airport IT systems. This gives flight crews current policies, procedures, and alerts, while traffic control systems coordinate which planes can take off at which times.
This same approach can effectively be used in healthcare. Automated protocols throughout the care plan can help providers pull relevant information from all necessary care teams and orchestrate operational processes in the background. Tasks can be completed in an efficient and timely manner, with managed expectations creating a seamless care pathway.
With a “care traffic control” approach, care teams manage by exception. Care plans are digitized, automated, and orchestrated across teams and settings, letting care teams be efficiently tasked at the right time and at the right place. Additionally, care teams can capitalize on virtual patient engagement techniques and will intervene only when manual engagement is needed.
In the seventh annual Health IT Industry Outlook Survey conducted by Stoltenberg Consulting Inc., 42 percent of health IT leaders rate updating technology to improve the patient experience as the top objective for 2019, followed by measuring improvement in patient care (33 percent).
Coinciding with this pivotal focus on empowering the patient care journey, 45 percent of respondents identify value-based care as the most significant, pressing topic in healthcare this year, followed by artificial intelligence (26 percent) and cybersecurity (20 percent). Meanwhile, leveraging meaningful patient data (32 percent) serves as the largest overall hurdle for health IT teams in 2019, followed closely by ineffective IT or EHR operations (29 percent).
In the push to gain true value in value-based care initiatives, lack of system interoperability stands as the biggest operational burden for healthcare organizations (54 percent), followed by rising overhead and staff costs (17 percent), financial reimbursements (15 percent) and EHR burnout or reporting burden (14 percent).
“Thanks to the continuing industry push for healthcare interoperability, significant progress is starting to come to fruition,” said Dan O’Connor, vice president of client relations at Stoltenberg Consulting. “We’re now seeing a clearer picture of how different players across the care spectrum will be held accountable to drive more transparent, engaged patient care journeys, which in turn will help healthcare providers meet their organizational goals.”
Other key survey findings indicate that despite nearly universal initial adoption across the country, EHR and application implementation support (34 percent) remains the top 2019 IT outsourcing request, followed by optimization work (27 percent), legacy system support (22 percent) and help desk support (17 percent). Yet, with current IT training offered, 63 percent of respondents say they feel “unprepared” or “very unprepared” to manage and execute effective IT operations within their healthcare facilities.
Stoltenberg conducted the survey at the 2019 Health Information and Management Systems Society (HIMSS) annual conference in Orlando. More than 300 survey participants represented a comprehensive spectrum of provider facilities, including health systems, standalone hospitals, physician practices and other ambulatory care facilities. Clinical IT professionals led survey participation (38 percent), while executive/C-suite leaders followed closely behind (36 percent).
New data on the state of value-based care in oncology has found that while community oncologists are optimistic about the beneficial potential of value-based care, they see a conflict between the need to decrease episode costs and the rising prices of the most innovative novel therapies.
In an effort to dig deeper into current attitudes toward new value-based reimbursement models and novel therapies in cancer care, Integra Connect surveyed leaders and decision-makers in oncology practices. Respondents represented practices with approximately 530 community oncologists, all of whom are participating in value-based care programs.
The survey results yield useful insights into how oncologists are dealing with rising drug costs in the era of value-based care, which makes practices financially accountable for improving the quality of patient care while also lowering the overall costs of cancer episodes. As drug prices continue to increase to new levels, driven in part by groundbreaking therapies, respondents indicated that it is becoming increasingly difficult to keep costs below value-based care program targets.
Other key themes surfaced by the survey include: expectations for the future of value-based cancer care; how drug costs are affecting treatment behaviors; what oncologists need from pharmaceutical manufacturers; the influence and effect of care pathways; and the value of and vision for precision medicine.
The number one challenge for making value-based care work: Rising drug costs
When asked about the number one challenge for making value-based care succeed in oncology, the majority of respondents (57 percent) cited managing the rising cost of drugs, including promising but expensive novel therapies. Beyond the context of value-based care, 93 percent of oncologists describe increasing drug costs as a priority issue impacting the overall well-being of their practices.
Value-based care is driving changes in cancer treatment choices
With oncologists increasingly accountable for the cost of entire episodes of care, a full 87 percent of survey respondents said that value-based care is causing them to think differently about drug choices, compared to their approaches during the fee-for-service era. When it comes to the choice of drug for an individual patient’s treatment regimen, oncologists assert that they remain as committed as ever to delivering the best clinical outcomes, regardless of impact on episode cost.
Nonetheless, more than three-quarters of oncologists indicated that they are making changes to how they and their practices choose treatment regimens under value-based care programs. A sizeable group (38 percent) says that it may change drug choices and opt for lower-cost therapies, but only when efficacy and toxicity remain the same. An equal percentage of oncologists voiced a desire to develop a deeper understanding of drug value, not just cost, that helps them understand the patient impact of therapies on an individualized level.
The 2019 HIMSS Annual Conference may be over, but that doesn’t mean an end to the pressing challenges and trends discussed at Orlando’s Orange County Convention Center. More than 42,500 people attended the conference — the majority of whom were C-suite executives and HIT professionals taking full advantage of the healthcare IT industry’s largest opportunity for networking, product promotions, continuing education and major announcements.
As always, there were a few subjects during HIMSS19 that generated significant buzz. Here are four of those trends that will remain key topics throughout the next year:
Healthcare data exchange
The release of two long-anticipated proposed rules on information blocking came just as HIMSS19 convened. The Centers for Medicare and Medicaid Services (CMS) and the Office of the National Coordinator for Health Information Technology (ONC) unveiled proposals that would require healthcare providers and plans to implement open data sharing technologies to support transitions of care. The first focuses on standardized application programming interfaces (APIs) and carries forward provisions from the 21st Century Cures Act.
Those associated with Medicaid, the Children’s Health Insurance Program (CHIP), Medicare Advantage and Qualified Health Plans in the federally-facilitated exchanges would have to provide patients with immediate electronic access to medical claims and other health information by 2020. Under a latter proposal, health information exchanges (HIEs), health IT developers and health information networks (HINs) can be penalized up to $1 million per information blocking violation, but providers are not subject to fines.
The goal of the proposals is to consider care across the entire continuum, giving patients greater control and understanding of their health journeys. This is interesting, given that HIMSS attendees who responded to Stoltenberg Consulting’s seventh annual HIT Industry Outlook Survey noted “lack of system interoperability” as one of their biggest operational burdens, and “leveraging meaningful patient data” as the IT team’s most significant hurdle this year. Thus, overcoming these challenges to meet the newly proposed mandates will likely dominate discussions during the remainder of 2019.
Interoperability, as it was envisioned, should be built on transparency and connectivity, allowing a patient’s critical health information to be easily accessible, regardless of where treatment is being administered. By creating an infrastructure that supports the sharing of patient data along the care continuum, hospitals, skilled nursing facilities (SNF) and long-term post-acute care (LTPAC) facilities can offer the best care possible. As a result, organizations that participate in interoperability best practices are positioned to become preferred providers.
Unfortunately, interoperability is still a work in progress for many organizations. While more than 95 percent of hospitals and 90 percent of office-based physicians are now utilizing electronic health record (EHR) platforms, many struggle with — or have reservations around — sharing information outside of their facility. As such, silos represent a great barrier to realizing a fully implemented state of interoperability.
The current data gap can drastically impact care. For example, a patient experiences a serious medical incident — such as a fall or stroke — and arrives at the hospital where staff may not have access to existing patient data which could inform the best delivery of care. Or perhaps they’re able to access that data, but not right away. Care is now delayed, which can be additionally concerning depending on the time-sensitivity of the patient’s condition.
Taking this example a step further, let’s explore what happens after care at the hospital has concluded. The patient requires rehabilitation, and a continuation of care document (CCD) is issued to a post-acute care facility. From there, the patient’s information is transferred by less-than-foolproof methods such as fax, for example. A glitch as simple as a jammed paper feed could prevent critical information from reaching the appropriate caregiver.
As value-based care and payment-care models are moving toward the forefront, blind handoffs of patient information are no longer viable, as they drastically increase the financial risks hospitals and payer groups are subject to — not to mention the clear detriment the system has on delivery of care.
Closing the gap
The larger question is how does the industry get from Point A to Point B? The easy answer is to liberate the data through a cloud-based infrastructure that supports an efficient, easy-to-access data exchange between all caregivers. An integrated solution would connect stakeholders across the care continuum, providing accurate insights when needed, eliminating data silos between care partners, and enabling more confident decision-making.
These systems would promote:
Optimized transitions: Data needs to travel with the patient — or before movement — discretely across all systems.
Patient visibility: Data should reflect the most current ADT information, identifying and sharing where a patient is and from where they’ve been discharged.
Central view of LTPAC patients: This facility-agnostic feature should offer automated updates of a patient’s functional progress.
Ongoing status and monitoring: Maintaining continued care is facilitated through alerts and notifications to caregivers regarding any change to their status or well-being and meaningful feedback on care pathway progress.
Facility performance: Beyond understanding a patient’s status, it’s also helpful to understand how facilities in and out of their PPN have performed.
The concept of interoperability, in some ways, seems contradictory to traditional best practices. Healthcare organizations are charged with protecting patient data at all costs, and the idea of sharing data in a way that opens access to a wider group of stakeholders could give pause. Regulatory infractions for data loss in the healthcare industry can be steep, and the number of well-publicized data breaches in recent years reinforces how valuable health records are to both the organizations who keep them and those who try to steal them.
So, it should go without saying that an EHR “superhighway” must be developed with security in its DNA, taking stringent regulatory requirements into account. The good news is that the newest breed of information exchange platforms is being built with security roles in mind, drastically reducing the possibility of data loss.
Decreasing inpatient admission volumes, shifts in the re-imbursement mix from higher-margin commercial payers to lower-margin public payers, and pressures resulting from value-based care have been solid trends during the past several years. Thus, it was not surprising that a Moody’s Investors Service report released in August portrayed the current condition of finances for not-for-profit hospitals as troubling.
According to Moody’s, the median annual expense growth rate slowed from 7.1 percent in 2016 to 5.7 percent in 2017 because of hospitals’ continued control of labor and supply costs. But annual revenue growth fell faster, from 6.1 percent in 2016 to 4.6 percent in 2017, the second straight year that expense growth exceeded revenue growth, a trend that is expected to continue through 2019. Moody’s concluded that not-for-profit hospitals are on an “unsustainable path.”
Consequently, median operating margins dropped to an all-time low of 1.6 percent in 2017. More than 28 percent of hospitals posted operating losses last year, up from 16.5 percent in 2016. Of course, operating losses cannot be sustained forever. If they are sustained for multiple years, closure of the hospital frequently results. Earlier this year, Morgan Stanley concluded that 18 percent of U.S. hospitals are at risk of closure or are weak financially, with approximately 8 percent of hospitals (roughly 450 facilities) presently at risk of closing. To put that figure in perspective, during the past five years, only 2.5 percent (150 hospitals) have closed. Also, Morgan Stanley found that 10 percent of hospitals suffer from weak finances.
Various factors account for not-for-profit hospitals’ financial difficulties.
Because the vast majority of net patient revenue came from fee-for-service based payment models—such as DRG payment, fee schedule, percentage of the chargemaster, or list price—overall reduced payment rates adversely impacted revenue in 2017. To be clear, nominal payment rates did not decline—e.g., Medicare’s Inpatient Prospective Payment System and Outpatient Prospective Payment System both incorporated nominal year-to-year increases in 2017—but the revenue mix for hospitals did shift from higher-margin commercial payers to lower-margin public payers. Median Medicare and Medicaid payments as a percentage of gross revenue rose to 45.6 percent and 15.5 percent, respectively, in 2017. Furthermore, continuing a five-year trend, public payers’ share of hospital revenue is projected to increase for the foreseeable future, as more of the baby boomers—an obviously large demographic group—reach retirement age and an increasing number of them incur the sizable costs of the last year of life.
In addition, hospital finances were adversely impacted by the continued shift from inpatient to outpatient care, a trend driven by greater competition from ambulatory facilities, such as physician offices and ambulatory surgery centers. Moody’s reported that median outpatient growth rates exceeded inpatient growth rates for the fifth straight year. In her July 25 address to the Commonwealth Club, Seema Verma, administrator of the Centers for Medicare & Medicaid Services, supported the inpatient-to-outpatient shift, stating that Medicare is seeking to avoid “downstream” expenses, such as emergency department (ED) visits and hospital admissions.
Faced with these financial challenges, not-for-profit hospitals have pursued a number of approaches.
Most commonly, they have tried to improve their management of labor and supply costs. However, this strategy—while certainly logical—may be reaching a point of diminishing returns. Lyndean Brick, president and CEO of the Advis Group, a healthcare consulting firm, has concluded: “This is no longer solely about expense reduction. If not-for-profits just focus on that, they will be out of business in the next few years” (Modern Healthcare, Aug. 29, 2018).
Another strategic response has been consolidation—in which small hospitals join a larger health system—to gain more leverage with payers, to accomplish greater economies of scale, to get access to lower-cost capital, and to enhance access to talent.
By Poornima Venkatesan, senior consultant, Virtusa.
In today’s value-based care environment, patient engagement is a vital key to success in clinical outcomes. This is especially true for chronic diseases such as arthritis, where continuous care is necessary because of the disease’s physical, emotional and economic impact on patients. Although the advent of specialty drugs in the past decade has made disease control possible, clinicians still face challenges in patient care because patients’ preferences about therapy aren’t often considered.
Understanding patient goals and expectations
While a clinician’s goal is to achieve remission, a patient’s goal could be clinical or nonclinical and varies depending on their individual characteristics and demographics.
Patients from low-income countries such as Morocco expect access to primary care (never mind rheumatologists), support services and education about the disease. The high expenses related to rheumatoid arthritis (RA) in such countries result in poor treatment compliance, school absenteeism in children and deterioration in quality of life. Comparatively, even with excellent health insurance systems in the United States, one in six adults with RA reduce their medication use because of high out-of-pocket costs. Most patients expect cost-effective care. In wealthier countries like the United Kingdom, patients expect increased social connectedness and family support.
Elderly patients expect reduced pain, fatigue and side effects, whereas young adults expect independence and normalcy from their treatments. Women, who are most affected by RA, might expect a lesser impact on family life and childrearing.
If such multidimensional expectations are not met, patients tend to discontinue their treatment. As new biologics and non-biological complex drugs (NBCDs) are developed, patient adherence is essential in determining both therapeutic and potential adverse effects. Studies reveal that frustration towards the method of drug administration (like self-injection) also impacts adherence. In the U.S alone, the total cost of non-adherence is estimated between $100 billion and $289 billion annually.
Therefore, it is important for the patient and the physician to trust each other and have open discussions about treatment strategies and expectations to ensure better alignment and cooperation.
Measuring patient engagement
The first step towards patient engagement is awareness of their current engagement levels. The patient activation measure (PAM) tool is helpful here. PAM measures the attitude and knowledge of patients about the disease and treatments. Studies have proven that highly activated patients have better outcomes via increased medication adherence, resulting in lower healthcare costs through fewer ED visits, hospital admissions and re-admissions. By continuously monitoring activation levels, providers can measure sustained changes in patient behavior and personalize their care programs.
We can also measure engagement levels by taking advantage of data. Data derived from direct [electronic health records (EHR), claims] and indirect sources (wearables) provide a holistic view of an individual patient. Simple analytics applied to population data can predict patient behavior. For example, analytics can help providers know which patients are likely to miss their appointments, which patients will fill their prescriptions on time, and so on. Detailed patient-based data could also lead to better and more accurate diagnoses and treatments.
By Richard A. Royer, chief executive officer, Primaris.
Back in the day – the late 1960s, when social norms and the face of America was rapidly changing – a familiar public service announcement began preceding the nightly news cast. “It’s 10 p.m. Do you know where your children are?”
Today, as the healthcare landscape changes rapidly with a seismic shift from the fee-for-service payment model to value-based care models, there’s a similar but new clarion call for quality healthcare: “It’s 2018. Do you know where your data is?”
Compliance with the increasingly complex alphabet soup of quality reporting and reimbursement rules – indeed, the fuel for the engine driving value-based car – is strongly dependent on data. The promising benefits of the age of digital health, from electronic health records (EHRs) to wearable technology and other bells and whistles, will occur only as the result of accurate, reliable, actionable data. Providers and healthcare systems that master the data and then use it to improve quality of care for better population health and at less cost will benefit from financial incentives. Those who do not connect their data to quality improvement will suffer the consequences.
As for the alphabet soup? For starters, we’re as familiar now with these acronyms as we are with our own birth dates: MACRA (the Medicare Access and CHIP Reauthorization Act of 2015), which created the QPP (Quality Payment Program), which birthed MIPS (Merit-based Incentive Payment System).
The colorful acronyms are deeply rooted in data. As a result, understanding the data life cycle of quality reporting for MACRA and MIPS, along with myriad registries, core measures, and others, is crucial for both compliance and optimal reimbursement. There is a lot at stake. For example, the Hospital Readmissions Reduction Program (HRRP) is an example of a program that has changed how hospitals manage their patients. For the 2017 fiscal year, around half of the hospitals in the United States were dinged with readmission penalties. Those penalties resulted in hospitals losing an estimated $528 million for fiscal year 2017.
The key to achieving new financial incentives (with red-ink consequences increasingly in play) is data that is reliable, accurate and actionable. Now, more than ever, it is crucial to understand the data life cycle and how it affects healthcare organizations. The list below varies slightly in order and emphasis compared with other data life cycle charts.
Find the data
Capture the data
Normalize the data
Aggregate the data
Report the data
Understand the data
Act upon the data
One additional stage, which is a combination of several, is secure, manage and maintain the data.
Find the data. Where is it located? Paper charts? Electronic health records (EHRs)? Claims systems? Revenue cycle systems? And how many different EHRs are used by providers — from radiology to labs to primary care or specialists’ offices to others providing care? This step is even more crucial now as providers locate the sources of data required for quality and other reporting.
Capture the data. Some data will be available electronically, some can be acquired electronically, but some will require manual abstraction. If a provider, health system or accountable care organization (ACO) outsources that important work, it is imperative that the abstraction partner understand how to get into each EHR or paper-recording system.
And there is structured and unstructured data. A structured item in the EHR like a check box or treatment/diagnosis code can be captured electronically, but a qualitative clinician note must be abstracted manually. A patient presenting with frequent headaches will have details noted on a chart that might be digitally extracted, but the clinician’s note, “Patient was tense because of job situation,” requires manual retrieval.
Normalize the data. Normalization ensures the data can be more than a number or a note but meaningful data that can form the basis for action. One simple example of normalizing data is reconciling formats of the data. For example, a reconciling a form that lists patients’ last names first with a chart that lists the patients’ first name first. Are we abstracting data for “Doe, John O.” or “John O. Doe?” Different EHR and other systems will have different ways of recording that information.
Normalization ensures that information is used in the same way. The accuracy and reliability that results from normalization is of paramount importance. Normalization makes the information unambiguous.
Aggregate the data. This step is crucial for value-based care because it consolidates the data from individual patients to groups or pools of patients. For example, if there is a pool of 100,000 lives, we can list ages, diagnosis, tests, clinical protocols and outcomes for each patient. Aggregating the data is necessary before healthcare providers can analyze the overall impact and performance of the whole pool.
If a healthcare organization has quality and cost responsibilities for a pool of patients, they must be able to closely identify the patients that will affect the patient pool’s risks. Aggregation and analyzing provides that opportunity.