• Skip to primary navigation
  • Skip to main content
  • Skip to footer
  • Blog
  • Contact Us
  • Request a Demo
  • Client Login
PointRight

PointRight

  • Solutions for
    • Skilled Nursing Facilities
    • Hospitals & Health Systems
    • Accountable Care Organizations
    • REITs
    • Health Plans
    • View All Solutions
  • Products
    • Performance Management
      • ScoreCard
      • Five-Star FastTrack
      • Pro 30 Rehospitalization
    • Care Management
      • RADAR
      • Data Integrity Audit (DIA)
      • PDPM
      • Quality Measures (QM)
    • State Value Based Programs
      • QASP (CA)
    • Training & Support
      • CEU Education
      • Product Training
      • Professional Services
    • View All Products
  • Events & Webinars
    • Events
    • Webinars
  • Industry Insights
    • Clinical
    • Regulatory
    • Reimbursement
    • Network Management
  • Our Company

Events

> Events & Webinars > Events

Blog

By now you have likely heard of CMS’s Accountable Care Organization Realizing Equity, Access, and Community Health (ACO REACH) Model. While priorities of the model include provider leadership, governance, and transparency, the most talked about is the push to advance health equity beginning 2023.

In the post-acute space, where the analytics adoption rate sits around 41%, providers are now being asked to integrate more data as part of their reporting and performance programs. Some of these are Social Determinants of Health (SDOH) related data, and with these new requirements come new questions.

  • How can we address conscious and unconscious bias in the Social Determinants of Health (SDOH) space?
  • How can we mitigate risk when working with these non-clinical data points?
  • What obligation do health systems have to payers to mitigate this risk?

I asked a few colleagues of mine to help weigh in on these questions drummed up by the ACO REACH model from a few vantage points.

Majd Alwan, Ph.D. 

SVP of Technology & Business Strategy, and Executive Director Center for Aging Services Technologies (CAST)

Majd Alwan – Healthcare Continuum Perspective

First and foremost, health systems and clinicians should:

  • strive to understand their patients’ SDOH including living setting, circumstances, the available care, services and support network available to their patients, and
  • work closely with the long-term and post-acute care, services, and community support organizations that touch their patients to gather information, share anticipated care and support needs, collaborate on refining shared care plans, and coordinating with them to ensure proper execution.

This is the only way to mitigate risk for everyone, starting with patients, and ending with all providers involved, including payers, under value-based care! 

Robert Latz, PT, DPT, CHCIO 

CIO, Trinity Rehabilitation Services

Bob Latz – SNF Provider Perspective

Conscious and unconscious bias happens all around us, whether we are using computerized systems or not, whenever we discuss data elements in the realm of SDOH. Recognition of the potential for bias is important. Recognition of the implicit bias in the current data we have already collected—and this influence on health—is one step forward.

Unless we ‘incentivize’ practices that move the needle toward whole patient centered care, we will not see improvement. In other words, IF the VBP model is set to incentivize having ‘limited numbers’ of individuals in certain SDOH categories, then we may see movement to minimize these patients to ‘improve scores.’ These might be conscious or unconscious actions in this direction.

For this reason, I believe this ‘obligation’ is upon all of us to advocate and encourage an equitable way to incentivize inclusivity instead of exclusivity…incentivize what we want to see happen. IF we get an action opposite of our intention, then tweak the incentivization until we see inclusivity regardless of any SDOH category.

Janine Savage, RN, CHC

Vice President, Product Management, Analytics and Business Intelligence, Net Health

Janine Savage – Analytics Vendor Perspective and Summary

SDOH is an increasingly important and emerging area in Analytics. Currently we have access to a few standardized MDS data elements that relate to SDOH, such as race/ethnicity and Medicaid as payer, and we have considered those in our model building. As SDOH data becomes more standardized and collected consistently throughout the healthcare continuum, we will incorporate that data into our post-acute analytics model building. 

When we build Analytics models, we use leading-edge statistical techniques and technologies to reduce bias to the extent possible. As my colleagues have noted, it is not possible to eliminate bias altogether – but by working together with transparency, we can mitigate its effects.

 

More from CMS on the ACO REACH Model: 

https://innovation.cms.gov/innovation-models/aco-reach

Download eBook on MDS-based Analytics

More on MDS-based Analytics

FREE EBOOK – Learn more about the insights buried in your facilities’ minimum data set. 

Download eBook

Filed Under: Blog

Benefits to SNFs, their partners and the healthcare continuum

We’ve covered predictive post-acute analytics in practice and tackling the challenges of the Triple-Aim. Now it’s time to bring it home by defining the value created by predictive analytics in the post-acute space. Like any investment of time and money, it’s important to be clear about the results of those investments. 

SNF Provider Perspective

Advanced analytics can help providers demonstrate their value to payers. Improving the quality of care improves the reputation of the provider in the community and having data to tell that story helps show that it’s happening, and these improvements are real and measurable.

It also benefits payers to know which providers can demonstrate their value and performance in ways that are quantifiable and clear.

"We have to know our data [before sharing with payers]...so we can tell our story behind it." - Bob Latz, PT, DPT, CHCIO, CIO at @trinityrehabsvc#ForcuraCONNECT #ForcuraWebinar

— Forcura (@Forcura) January 19, 2022

Healthcare Continuum Perspective

We’ve found that users of our Data Integrity Audit have been able on average to increase their reimbursement by $4.16 per resident per day by catching coding errors and inconsistencies. Imagine that change in reimbursement of $4.16 multiplied across a continuum of providers. That’s where these analytics start making an impact at scale.

If providers speak in clinical outcomes and payers speak in dollars and cents, advanced analytics allow the payer and provider to speak a common language. It’s important to invest in the analytics that bring these relationships together and create a path forward. It’s also important to share stories of those that are succeeding so the adoption rate of these advanced analytics increases and the need for demonstrable ROI is reduced as the value of these analytics investments becomes a known quantity.

"When you demonstrate competency [through predictive analytics]...that increases the volume of referrals to your facility compared to your competitors." - Majd Alwan, Ph.D., @LeadingAge #ForcuraCONNECT #ForcuraWebinar

— Forcura (@Forcura) January 19, 2022

Analytics Provider Perspective

Analytics vendors and SNF providers that collaborate effectively can demonstrate the return on their investment in post-acute analytics in many ways. It’s important to partner with vendors who can provide you with the right information and the right experts to avoid analysis paralysis.

The more literal, or ‘hard ROI,’ can be demonstrated in dollars and cents, like that additional $4.16 per resident per day in reimbursement. That’s clear and demonstrable. The more elusive to quantify ‘soft ROI’ can be found in the value of better care planning as felt by the resident and families, improved Care Compare ratings, and clinical quality improvements that payers notice.

[ More on the ROI of Data Analytics for SNFs ]

"Liberating data with a partner that can help you analyze it is important." - Jane Moffett, VP, Product Management, Therapy at @NetHealthFits #ForcuraCONNECT #ForcuraWebinar

— Forcura (@Forcura) January 19, 2022

Predictive Analytics and Value – In Summary

This series of blogs on predictive analytics and value-based care is inspired by a recent panel session – Predictive Analytics to Power Up SNF Performance – and aims to encourage excitement for and adoption of advanced analytics in the post-acute space. Thank you to my colleagues and fellow panelists Bob Latz (Trinity Rehab Services), Majd Alwan (LeadingAge), and Jane Moffett (Net Health) for contributing to this conversation. 


Predictive Analytics Resources

Data Analytics Selection Tool
LeadingAge

Improving Health Outcomes, Resident Experience and Quality through Data Analytics
LeadingAge/ArchCare/PointRight Case Study

Turn Your EMR Data into Valuable Information
Net Health Blog

Filed Under: Blog

Advanced post-acute analytics and value-based care

Now that we’ve covered predictive post-acute analytics in practice, we move on to the topic of tackling the challenges of the Triple-Aim. 

This series of blogs on predictive analytics and value-based care is inspired by a recent panel session – Predictive Analytics to Power Up SNF Performance – and aims to encourage excitement for and adoption of advanced analytics in the post-acute space. Thank you to my colleagues and fellow panelists Bob Latz (Trinity Rehab Services), Majd Alwan (LeadingAge), and Jane Moffett (Net Health) for contributing to this conversation. 

SNF Provider Perspective

Providers are using post-acute analytics to guide their decision-making and succeed in value-based care arrangements with payers today. Referral sources are looking at provider data and you want to be ready to tell your story behind it. Rehospitalization is a big point of collaboration amongst provider partners and an area where predictive measures can identify opportunities to intervene as early as possible. As a SNF Provider, showing the impact of improved outcomes on the tenets of the Triple Aim sends a positive message to their payer partners.

"The value of [predictive analytics] is...improved care across the entire system." - Bob Latz, PT, DPT, CHCIO, CIO at @trinityrehabsvc #ForcuraCONNECT #ForcuraWebinar

— Forcura (@Forcura) January 19, 2022

Healthcare Continuum Perspective

Predictive analytics help meet the challenges of the Triple Aim and value-based care because you need to demonstrably advance the needle on all three fronts: improve quality of care of your residents, reduce the cost of care for the residents or population, and at the same time increase the satisfaction of your residents and their families. With the CMS focus on paying for performance, the post-acute space in general has been somewhat at a disadvantage.

The regulators are using analytics to benchmark us, and providers are using these benchmarks to create their networks, so if you are not looking at these metrics for performance, you will be at a disadvantage. Insights from analytics are key to making the right decisions, particularly future facing ones. Benchmarking against your peers is essential.

"If we're not aware of our performance and not taking steps to reduce hospitalizations & improve the way we care for certain populations...we're going to be at a disadvantage." - Majd Alwan, Ph.D., @LeadingAge #ForcuraCONNECT #ForcuraWebinar

— Forcura (@Forcura) January 19, 2022

Analytics Provider Perspective

Providers can use post-acute analytics as a competitive differentiator in value-based care by tracking and sharing KPIs that matter most to referral partners and payers.

A recommended KPI starter set includes:

  • hospital readmission rate,
  • risk adjusted length of stay,
  • risk adjusted return to community rate,
  • and post-acute care volume.

Risk adjustment is so important because that allows a facility to compare themselves over time to their own performance and their peers.

You never want to be in a meeting compared to other providers and that’s your first time seeing the data. And where you’re performing well, you want to tell that story; and where you’re not performing well, analytics should help you discover why and devise a plan. Take that into your Quality Assurance and Performance Improvement (QAPI) program.

"[Predictive analytics] at the patient level and the staff level have reduced hospitalization...and made a difference in peoples' lives." - Janine Savage, RN, CHC, VP of Product Management, Analytics & Business Intelligence, @NetHealthFits #ForcuraCONNECT #ForcuraWebinar

— Forcura (@Forcura) January 19, 2022

Next level of KPIs would include key clinical quality measures that lead to and inform those utilization outcomes like falls, pressure ulcers, antipsychotic medication use and other things that concern payers.

Predictive Analytics and Triple Aim – In Summary

Access to predictive analytics in the post-acute space benefits both SNF providers and payers who are all under the same pressure to meet the triple challenge of increasing healthcare quality, reducing healthcare cost and improving the patient experience. Risk identification and early intervention through predictive analytics are key elements in tackling these challenges.


 

Next Topic in Predictive Analytics Series…

  • Predictive Analytics and Value-Based Care: Defining Value 

Predictive Analytics Resources

Data Analytics Selection Tool
LeadingAge

Improving Health Outcomes, Resident Experience and Quality through Data Analytics
LeadingAge/ArchCare/PointRight Case Study

Turn Your EMR Data into Valuable Information
Net Health Blog

Filed Under: Blog

Improving post-acute outcomes and care team experience 

I recently participated in a panel discussion that explored the value that predictive analytics bring to skilled nursing facilities (SNFs), their partners, and ultimately to the healthcare continuum – Predictive Analytics to Power Up SNF Performance.

This series of blogs on predictive analytics and value-based care aims to encourage excitement for and adoption of advanced analytics in the post-acute space. Thank you to my colleagues and fellow panelists Bob Latz (Trinity Rehab Services), Majd Alwan (LeadingAge), and Jane Moffett (Net Health) for contributing to this conversation. 

"Analytics is so much more than just a digital chart. It's a way of thinking to...help make patient care better." - Jane Moffett, VP of Product Management, Therapy at @NetHealthFits #ForcuraCONNECT #ForcuraWebinar

— Forcura (@Forcura) January 19, 2022

SNF Provider Perspective  

Providers currently have access to PAC analytics dashboards and alerts and can use these tools on a regular basis to decrease rehospitalization by identifying risk and intervening earlier.

Alerting requires that documentation is entered in accurately so being informed of missing documentation, coding errors and so on is part of being able to form the basis for these important predictive models. The adage ‘garbage in, garbage out’ applies here – you want sound data at the heart of your analytics and insights.  

 

"Many [SNFs] use dashboards, alerts and tools with predictive analytics to decrease hospitalizations for individuals who are at-risk and to [prevent adverse events]." - Bob Latz, PT, DPT, CHCIO, CIO at @trinityrehabsvc#ForcuraCONNECT #ForcuraWebinar

— Forcura (@Forcura) January 19, 2022

Healthcare Continuum Perspective 

In terms of adoption across the healthcare continuum, the adoption rate of data analytics has increased from 31% to 41% in the last 2 years. This is encouraging but still falls short of the omnipresence of technologies like the EHR.

One of the impediments to adoption has been access to and integration of post-acute data. We know that insights from multiple sources of data will yield richer decision support and thus moving towards data liberation and integration is key.  

"We've seen a 10% jump in the adoption of predictive analytics in the last year." - Majd Alwan, Ph.D., SVP of Technology & Business Strategy, Executive Director Center for Aging, @LeadingAge#ForcuraCONNECT #ForcuraWebinar

— Forcura (@Forcura) January 19, 2022

Analytics Provider Perspective

We know that using post-acute analytics improves the clinician and care team experience of providing care because we dialog with our users about how analytics are adding value and how are they becoming part of their workflow.

We’ve learned three requirements for analytics adoption and customer satisfaction.

  • Integration into the workflow in a way that’s balanced between their existing workflows and changing the workflow based on new information 
  • Information must be actionable with insights into recommended actions or potential risks 
  • Results need to be evident at the patient and facility level  

Predictive Analytics in Practice – In Summary

Early adopters of predictive analytics in the post-acute space play a vital role in boosting adoption rates and shaping the voice of the SNF in healthcare continuum conversation. Shining a light on successful providers with real world outcomes, and more importantly on successful provider/payer partnerships with demonstrable ROI, will be vital to making predictive analytics as common a part of the care experience as the EHR.  


 

Next Topics in PointRight’s Predictive Analytics Series…
  • Predictive Analytics and Value-Based Care: Tackling Triple Aim
  • Predictive Analytics and Value-Based Care: Defining Value

Predictive Analytics Resources

Data Analytics Selection Tool
LeadingAge

Improving Health Outcomes, Resident Experience and Quality through Data Analytics
LeadingAge/ArchCare/PointRight Case Study

Turn Your EMR Data into Valuable Information
Net Health Blog

Filed Under: Blog

Staying competitive and relevant in the world of healthcare is a constant battle as the landscape is always changing. One of the biggest shake-ups in the past few years is the way treatment is reimbursed with a shift from the traditional fee-for-service model to value-based payment. In response, many healthcare organizations are navigating the new waters and finding success through partnership and collaboration.

Download the Case Study

Why is this important? By studying the fruits of these partnerships, healthcare organizations can not only find new ways to stay competitive, but they can dramatically improve patient care and drive better patient outcomes.

One of the most recent examples of partnerships driving results comes from the state of New Mexico. The New Mexico Human Services Department (HSD), the New Mexico Health Care Association (NMHCA), nursing home provider leadership, and the state’s three Medicaid managed care organizations (MCOs) formed a collective workgroup to tackle the new challenges together. Forming the New Mexico Nursing Facility Value Based Payment (NF VBP) Workgroup, a review of the collaboration shows us how properly leveraged data, communication, and transparency can effectively drive improved patient care and better patient outcomes.

The Challenge Overview

The NF VBP knew it was facing a unique set of shared challenges including an aging population with complex needs, logistical challenges from geographically dispersed facilities, and data collection hurdles making it hard to effectively benchmark, foster growth, and meet the demands mandated by the Centers for Medicare and Medicaid Services (CMS). Failure to meet these challenges can  spell trouble, especially in a highly competitive industry where the competition also understands the importance of innovation.

Download the Case Study

A Solution in Plain Sight

Sometimes identifying a solution and achieving a solution are two totally different beasts. The NF VBP knew the solution to their challenges was data. By finding a way to collect meaningful data in near real-time, the workgroup would be better equipped to make the necessary changes, deploy the right new processes, and respond to the ever-changing healthcare landscape better and faster.

However, the solution wasn’t that simple. Because of things like the complexity of each of the workgroup member’s operations, the challenges of the MDS workflow, and concerns of new burdens on staff, finding a streamlined solution seemed like an impossible task.

A Second Partnership

Luckily, a solution existed to not only meet, but exceed the high demands of the NF VBP workgroup. PointRight®, a Net Health company, delivered an advanced analytics solution that offered all the necessary data the group needed in near real-time, and in a way that easily integrated with existing systems and didn’t bring additional burdens on staff.

The results? Transparency, an improved patient experience, and satisfied customers. With the ability to facilitate care coordination from the top-down, collect and aggregate data by facility, corporation, and MCO, identify and meet unique quality measures, and so much more, the NF VBP has established itself in a strong position to meet and exceed the challenges of today and the future.

New Mexico Nursing Facility VBP Case Study

If you’d like to learn more details, we’d invite you to check out a free copy of the complete case study titled—New Mexico Nursing Facility Value Based Payment (NF VBP) Workgroup Partners with PointRight® to Improve Patient Care and Outcomes.

Download New Mexico VBP Case Study

Filed Under: Blog

In today’s value-based care environment, long-term post-acute analytics (LTPAC) have become essential for skilled nursing facilities (SNFs) to succeed, stay competitive and achieve the objectives of the Triple Aim for participation in value-based care initiatives. Predictive analytics are changing healthcare with insights that help clinicians identify patients at risk for adverse events and poor health outcomes. Analytics from clinical data provide decision-making support to facilitate the implementation of care pathways and evidence-based best practices to manage clinically complex patients with multiple comorbidities.

post acute performanceHigh-performing predictive algorithms go beyond the identification of single risk factors. They also consider interdependent factors that contribute to the outcome. For example, predictors such as weight and conditions like diabetes and hypertension, especially when multiple comorbidities are present, tend to negatively impact a wound’s likelihood of healing.

Predictive algorithms can be created leveraging data from both EHR and Minimum Data Set (MDS) data. Predictive analytics derived from EHR and MDS data are best used in a complementary way, providing comprehensive insights to guide patient care. In this blog, we examine how EHR- and MDS-based analytics each bring value and the potential synergy that exists when both are used together.

The Merits of EHR Data-Based Analytics

EHR data-based analytics are primarily used to identify and manage acute changes in a patient’s condition so a clinician can further assess the patient and intervene to manage and prevent complications. For example, a patient develops a fever, declines slightly in activities of daily living self-performance, and has decreased fluid intake. An EHR-based predictive algorithm may identify these descriptive indicators and determine that the patient is at risk for readmission to the hospital within the next few days. Once alerted to this change in condition and imminent risk, the clinician can perform an assessment so the care team can intervene quickly to treat the patient and mitigate the risk.

There are some drawbacks to EHR-based analytics. Because post-acute EHR data is not yet standardized and is proprietary to each EHR vendor, the size of the data sets and benchmarking capabilities may be limited. Much EHR data is not structured (e.g., progress notes are largely free text), which is challenging to use in predictive models. Developing algorithms may require the use of additional technologies, such as optical character recognition (OCR), to identify predictors – and they may be less reliable as a result. In addition, analytics is not a core competency for most post-acute EHR vendors.

The Merits of MDS Analytics Data

MDS-based patient-level analytics are used to manage the risk of adverse outcomes through the interdisciplinary assessment and care planning process. MDS analytics can help clinicians avoid adverse events like falls, pressure ulcers, and rehospitalization by developing individualized care planning interventions based on a patient’s risk factors. While it may be possible to work on eliminating some risk factors, others may need to be managed to prevent complications.

Unlike EHR data, MDS data is highly structured and standardized. Driven by comprehensive clinical and functional assessments of the patient, the data allows the development of valid and reliable predictive models that are clinically relevant with robust benchmarking capabilities. Analytics-focused companies have a core competency in data science and analytics.

Download eBook on MDS-based Analytics

The Complementary Partnership of EHR- and MDS-Based Analytics

While EHR data and MDS-based analytics are effective individually, they’re not mutually exclusive when it comes to providing the best patient care possible. When used together, clinicians get the right analytics, at the right time, for the right purpose, which ultimately results in the right decisions for patients.

A great example of this is the way these analytics can be used in a complementary way to prevent rehospitalization, a key performance outcome for SNFs. A patient with a chronic lung disease in the SNF for a short-term rehabilitation stay following a hospitalization is identified to be at moderate risk for readmission by the MDS-based predictive algorithm with predictors including COPD, CHF, oxygen therapy, and incontinence.

The interdisciplinary care team considers these factors when developing the patient’s care plan, with a focus on managing them and preventing an acute exacerbation of her chronic illness. During the patient’s stay, she develops a mild cough and worsening shortness of breath. An EHR-based predictive algorithm identifies that she is now at high risk for readmission to the hospital and alerts the care team.  A nurse assesses the patient, notifies her physician, and interventions are quickly ordered to manage the acute exacerbation of symptoms in the SNF to prevent a rehospitalization.

When used together, these two types of analytics provide a much greater opportunity to reduce rehospitalization. A recent case study on Archcare, the continuing care community of the Archdiocese of New York, does a masterful job of showcasing this in a real-world example.

The Next Step

If you’re already using EHR data or MDS analytics individually, great! However, if you want to get the most analytics power from all of your data, we’d encourage you to explore technology-driven solutions to activate this complementary relationship and achieve better patient outcomes and even greater value.

Net Health does a masterful job of providing easy-to-integrate, clearly actionable, and highly effective solutions. At Net Health, analytics is a core competency and our data science and analytics experts are industry thought leaders. Choosing the right analytics partner is key to realizing the value analytics can bring. If you’d like to learn more about our PointRight Post-Acute analytics solutions and how they complement your EHR-based analytics, we’d encourage reaching out to schedule a demo and consultation today.

Filed Under: Blog

  • Go to page 1
  • Go to page 2
  • Go to page 3
  • Interim pages omitted …
  • Go to page 11
  • Go to Next Page »

Footer

  • Solutions for…
  • Products
  • Events & Webinars
  • Industry Insights
  • Our Company
  • Blog
  • Contact Us
  • Terms of Use & Privacy
  • Request a Demo
  • Lock icon   Client Login

Follow Us

Net Health Headquarters
40 24th Street, 1st Floor
Pittsburgh, PA 15222
800.411.6281  |  Fax: 412.261.2210
PointRight logo
NetHealth Logo
©2022 PointRight Inc., A Net Health Company. All Rights Reserved.
MENU logo
  • Solutions for…
    • Skilled Nursing Facilities (SNFs)
    • Hospitals & Health Systems
    • Accountable Care Organizations (ACOs)
    • REITs
    • Health Plans
  • Products
    • Performance Management
      • ScoreCard
      • Dashboard
      • Five-Star FastTrack
      • Pro 30 Rehospitalization
    • Care Management
      • RADAR
      • Data Integrity Audit (DIA)
      • PDPM
      • Quality Measures (QM)
    • State Value Based Programs
      • QASP (CA)
    • Training & Support
      • CEU Education
      • Product Training
      • Professional Services
  • Events & Webinars
    • Events
    • Webinars
  • Industry Insights
    • Clinical
    • Regulatory
    • Reimbursement
    • Network Management
  • Blog
  • Contact Us
  • Request a Demo
  • Client Login