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How to Increase Cash Flow Using Data and Analytics

In today’s challenging environment, healthcare leaders must seek opportunities to boost revenue through improved financial performance and reimbursement. Some common strategies include reducing the number of outstanding bill hold accounts, reducing A/R days, and managing discharged not final billed (DNFB) cases. This article tackles, the following topics:

  • Common reasons accounts remain unbilled.
  • Identifying opportunities for improvement.
  • Using data analytics and process improvement to achieve financial goals.
  • Creating lasting improvements.

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Physician Burnout and the EHR: Addressing Five Common Burdens

So far, the EHR hasn’t delivered on its original intent to improve patient care with more efficiency and personalization and lower cost. Instead, physician users blame the systems for worsening their experience and the quality of their care in significant ways:

  1. Less time for patient interaction and worsened quality of interaction.
  2. An extended workday.
  3. Poor design (difficult to use).
  4. Demands of quality measures.
  5. Cost and maintenance.
Despite these challenges, the EHR is likely here to stay. Health systems have invested heavily in their electronic reporting systems and are now focused on making these technologies and processes work for the benefit of patients and providers. CIOs are working towards better aligning digital health goals with physician experience for an environment where EHRs enable smarter, not harder, work.

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Weekly News Roundup: April 12, 2019

Employer-provided health insurance continues to be a hot topic-and a problem-for both providers and consumers. This week's news roundup examines the issue, including why employers provide health insurance in the first place, why Europeans don't get huge medical bills, how some employers are choosing their employees' doctors, and how data will help shape the future of employer insurance.

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The Future of Employer Health Insurance

Employers are always looking for ways to reduce one of their biggest expenditures–the cost of providing health insurance to employees. Many employers have explored solutions such as adding wellness plans, reducing usage, and providing different provider access mechanisms, all with modest success. Stemming the rising costs of health insurance requires management to understand and improve healthcare outcomes for their employee and dependent populations. Changing the future of employer health insurance will require a multi-faceted approach:

  1. Driving additional value by reducing utilization of healthcare services within these employer populations.
  2. Utilizing a wider lens through which to view performance of various providers, then making decisions based on those who are consistently providing low cost, high quality care.
  3. Employer will need to combine their data with other companies across a geographic region to get a better picture of the provider landscape than has ever been possible before.

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Five Action Items to Improve HCC Coding Accuracy and Risk Adjustment With Analytics

A hot topic in healthcare right now, especially in the medical coding world is the Hierarchical Condition Category (HCC) risk adjustment model and how accurate coding affects healthcare organizations’ reimbursement. With almost one third of Medicare beneficiaries enrolled in Medicare Advantage plans, it’s more important than ever for healthcare organizations to pay attention to this model and make sure physicians are coding diagnoses appropriately to ensure fair compensation. This article walks through basics of the risk adjustment model, why coding accuracy is so important, and five action items for interdisciplinary work groups to take. They include:

  1. Having an accurate problem list.
  2. Ensuring patients are seen in each calendar year.
  3. Improving decision support and EMR optimization.
  4. Widespread education and communication.
  5. Tracking performance and identifying opportunities.

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Weekly News Roundup: April 5, 2019

This week's news roundup is all about data and the life science industry: why extended real-world data is the industry's number one asset, why pharma R&D needs to adopt a data science mindset, how the cloud can transform pharma, and, on the flip side, why the industry is resistant to embrace cloud technology. Lastly, the Novartis CEO who wanted to bring tech into pharma now explains why it's so hard.

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Interoperability in Healthcare Data: A Life-Saving Advantage

When health system clinicians make care decisions based on their organization’s EHR data alone, they’re only using a small portion of patient health information. Additional data sources—such as health information exchanges (HIEs) and patient-generated and -reported data—round out the full picture of an individual’s health and healthcare needs. This comprehensive insight enables critical, and sometimes life-saving, treatment and health management choices. To leverage the data from beyond the four walls of a health system and combine it with clinical, financial, and operational EHR data, organizations need an interoperable platform approach to health data. The Health Catalyst® Data Operating System (DOS™), for example, combines, manages, and leverages disparate forms of health data for a complete view of the patient and more accurate insights into the best care decisions.

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Healthcare NLP: The Secret to Unstructured Data’s Full Potential

While healthcare data is an ever-growing resource, thanks to broader EHR adoption and new sources (e.g., patient-generated data), many health systems aren’t currently leveraging this information cache to its full potential. Analysts can’t extract and analyze a significant portion of healthcare data (e.g., follow-up appointments, vitals, charges, orders, encounters, and symptoms) because it’s in an unstructured, or text, form, which is bigger and more complex than structured data. Natural language processing (NLP) taps into the potential of unstructured data by using artificial intelligence (AI) to extract and analyze meaningful insights from the estimated 80 percent of health data that exists in text form. Though still an evolving capability, NLP is showing promise in helping organizations get more from their data.

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Weekly News Roundup: March 29,2019

  How are 2019 Predictions Holding Up? As the first quarter comes to a close, we’re taking a look how predictions for 2019 are holding up so far. This week, our news roundup examines population health trends to watch, healthcare predictions for 2019 to question, hospital revenue cycle trends, and how trends in appointment-based-businesses will affect healthcare this year. Population Health Trends to Watch, Trends to Question in 2019 healthcare organizations cannot afford to ignore consumers in 2019, as a number of major trends shape the …

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Four Steps to Effective Opportunity Analysis

Opportunity analysis uses data to identify potential improvement initiatives and quantifies the value of these initiatives—both in terms of patient care benefits and financial impact. This process is an effective way to find unwarranted and costly clinical variation and, in turn, develop strategies to reduce it, improving outcomes and saving costs along the way. Standardizing the opportunity analysis process makes it repeatable and prioritizes actionable opportunities. Quarterly opportunity analysis should follow four steps:

  • Kicking off the analysis by getting analysts together to do preliminary analysis and brainstorm.
  • Engaging with clinicians to identify opportunities and, in the process, get clinician buy in.
  • Digging deeper into the suggested opportunities to prioritize those that offer the greatest benefits.
  • Presenting findings to the decision makers.

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Extended Real-World Data: The Life Science Industry’s Number One Asset

The life science industry has historically relied on sanitized clinical trials and commoditized data sources (largely claims) to inform its drug development process—an under-substantiated approach that didn’t reflect how a new drug would affect broader patient populations. In an effort to gain more accurate insight into the patient experience and bring drugs to market more efficiently and safely, the industry is now expanding into extended real-world data (RWD). To access the needed breadth and depth of patient-centric data, life science companies must partner with a healthcare transformation company that has three key qualities:

  1. A broad and deep data asset.
  2. Extensive provider partnerships.
  3. An outcomes-improvement engine to support the next generation of drug development.

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Weekly News Roundup: March 22, 2019

Alongside widespread efforts to improve healthcare quality, safety, and interoperability, the demand for electronic health information exchanges (HIEs) is increasing. This week’s news roundup features news about HIEs, including the case for combining HIE data with an analytics platform, how HIEs can enable public health reporting when EHRs fall short, how they can help remedy long emergency stays, why mental health providers oppose behavioral health EHR requirements, and the great conundrum of data liquidity in healthcare.

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The Top Five 2019 Healthcare Trends

Bobbi Brown, MBA, and Stephen Grossbart, PhD have analyzed the biggest changes in the healthcare industry and 2018 and forecasted the trends to watch for in 2019. This report, based on their January 2019, covers the biggest 2019 healthcare trends, including the following:

  • The business of healthcare including new market entrants, business models and shifting strategies to stay competitive.
  • Increased consumer demand for more transparency
  • Continuous quality and cost control monitoring across populations.
  • CMS proposals to push ACOs into two-sided risk models.
  • Fewer process measures but more quality outcomes scrutiny for providers.

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Pairing HIE Data with an Analytics Platform: Four Key Improvement Categories

Population health and value-based payment demand data from multiple sources and multiple organizations. Health systems must access information from across the continuum of care to accurately understand their patients’ healthcare needs beyond the acute-care setting (e.g., reports and results from primary care and specialists). While health system EHRs have a wealth of big-picture data about healthcare delivery (e.g., patient satisfaction, cost, and outcomes), HIEs add the clinical data (e.g., records and transactions) to round out the bigger picture of patient care, as well as the data sharing capabilities needed to disseminate the information. By pairing HIE capability with an advanced analytics platform, a health system can leverage data to improve processes in four important outcomes improvement areas:

  1. Workflow
  2. Machine learning
  3. Professional services
  4. Data governance

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Weekly News Roundup: March 15, 2019

The healthcare industry employs more Americans than any other industry, and, in 2018, created one in every seven new jobs. This week’s news roundup is all about healthcare jobs-the fast paced of growth, why CIOs went from back-office operators to mission-critical innovators, how medical writers can help speed outcomes improvement, and how to turn data analysts into one of the industry’s most in-demand professionals-data scientists.

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Customer Journey Analytics: Cracking the Patient Engagement Challenge for Payers

Customer journey analytics uses machine learning and big data to track and analyze when and through what channels customers interact with an organization, with an aim to influence behavior (e.g., buying behaviors among retail customers). Similarly, healthcare organizations want to influence health-related behaviors, such a taking medication as prescribed and not smoking, to improve outcomes and lower the cost of care. In a partnership with an analytics services provider, a payer organization is leveraging customer journey analytics among healthcare consumers to identify the best opportunities and channels for patient outreach. With this analytics-driven engagement strategy, the payer has found an opportunity to significantly improve patient engagement—a predicted overall increase from 18 percent to 31 percent.

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