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As we continue to respond to Covid-19 and prepare for the next crisis, the health care community should learn from defense and intelligence agencies that have developed effective strategies to create a common operating picture through robust data integration.
Health systems across the country will require a plan to react to government deep spending cuts and revenue shortfalls due to the COVID-19 pandemic. Hospital services have seen a significant downturn in demand in 2020, and the recent resurgence in cases has led to further decreases. The public health emergency has also resulted in innovation, most notably telehealth, which has been granted temporary pay parity during the pandemic.
Still, hospitals and health systems face a year of losses, but COVID-19 consequences like enhanced communication and flexible decision-making make now the time to adopt a cost-reduction care delivery model. I propose a widespread overhaul of all aspects of the care model to achieve system-wide cost-reduction. This should include using data analytics and feedback to evaluate service lines, the transition to low-cost care methods, and realign partners and payers towards a cost-reduction model.
Success will be measured by cost savings and patient satisfaction in permanently changing patient expectations, especially as payers continue the shift towards value-based reimbursement. This highly customizable model I propose can be tailored to any health system.
1. Evaluate current service lines.
Transforming each service line to a lower cost-of-care setting may not be possible. Those lines that are not bringing in stable revenue and are resistant to low-cost settings represent a drain on resources at a time where minimizing losses is paramount. Health systems should evaluate their existing services portfolio using data analytics, considering revenue, patient satisfaction, outcomes, operating costs, and potential for transformation to a lower cost-of-care setting.
2. Eliminate nonprofitable lines and manage costs of essential lines.
Because health systems today tend to be highly consolidated, there may be a wide range of profitable, nonprofitable, and essential lines. Nonprofitable lines that do not support quality patient outcomes and incur high operating costs or low reimbursement rates should be eliminated from the service mix. Reevaluating essential services can also reveal ways to manage high costs. This step results in a more focused and efficient heath system with reduced waste and an improved service portfolio.
3. Transition service lines to appropriate low-cost-of-care settings.
Each service line must be fit to an appropriate low-cost care model, like telehealth. The public health emergency made transitioning to virtual healthcare a necessity with the cost and patient satisfaction benefits it presents. Again, the use of big data and data informatics are essential in identifying where transitioning to virtual platforms is advantageous. Other strategies can be used in conjunction with telehealth, like preventative health service lines to reduce ER overutilization and rehospitalizations. Not all service lines will be conducive to a virtual setting. Alternate cost-effective and successful care settings include home healthcare, mobile healthcare units, home monitoring, and out-of-network partnerships.
4. Assess the efficacy of new and existing virtual health technology.
Existing virtual capabilities developed during the pandemic must be evaluated in a post-COVID-19 setting. Not all processes should be kept in this transition, and ineffective technologies could represent a further drain on hospital revenue without producing favorable results.
5. Measure accuracy of diagnostic and treatment equipment and replace the obsolete.
Obsolete technology represents money trapped in assets, especially real estate. Although investing in new technology raises costs in the short-term, the cost-reduction benefits that arise from lower operating costs, more efficient and accurate testing and diagnosis, and less unnecessary testing will more than makeup for the initial investment. Health systems should consider the investment of money and time in relation to the benefits when deciding which service lines should receive updated equipment. Again, data analytics can assess current asset performance and present opportunities for renovation.
6. Establish patient-centered care tactics.
Patient-centered care aligns with value-based care trends arising in payer reimbursement plans. This approach to care delivery reduces waste and unnecessary treatments, decreasing healthcare costs1 and improving patient outcomes and engagement2,3. Orienting the workforce towards delivering patient-centered care is a matter of culture change. Providers are encouraged to cut down on treatment volume and focus on treatment quality, saving the patient and the provider time and money.
7. Optimize provider workflow in context with the new care model.
The workforce will also require reeducation to operate new workflows associated with the cost-reduction model. Office setups, electronic health records, billing, and other aspects of workflow logistics will have to be rewritten. Modifications will require coordination from provider feedback, health IT, administrative staff, and reimbursement to be instituted efficiently into the everyday workflow.
8. Integrate COVID-19 dashboards and reassign underutilized staff to communicate and enforce the new care model.
Health systems have opened communication lines that directly connect management and decision-making boards to all members of the workforce. The urgency in relaying decisions was paramount during the health crisis, and it will remain crucial to facilitate the execution of any changes to workflow and treatment protocols that accompany the new care model. Reassigning underutilized staff to enforce new policies can help monitor the care model’s progression.
9. Revisit partnerships and advocate for interoperability.
Health systems must shift to a “coopetition” model and operate through strategically aligned partners. Health systems should revisit partnerships with external agencies to determine if there are any opportunities to negotiate or modify the agreement to match the new care model and mutually benefit both parties. Where no advantageous partnerships exist, expand in-network capabilities. Partnerships are also a tool to expand coverage in areas that are in high demand post-pandemic, including home care and mental and behavioral health. Interoperability application programming interfaces (APIs) ensure there is effective and safe coordination of care between partners and in-network providers, which will significantly decrease administrative stress and facilitate secure transitions of care in addition to added capabilities with remote monitoring devices on the rise.
10. Leverage payment parity for telehealth from payers.
Many fear that telehealth payment parity policies will not be upheld after the pandemic. Records of cost-reduction, higher patient outcomes, and stimulated patient and workforce engagement should be kept as evidence to leverage better telemedicine service coverage from payers. Health systems should freely share their successful results with payers and other health systems to encourage industry-wide virtualization and telehealth reimbursement. In fact, consortiums such as the Alliance for Connected Care exist with a public call for data to support concerted efforts.
11. Constantly reevaluate and improve.
More than ever, patients are showing drastic changes in their care delivery expectations, and health systems must be open to receiving feedback from consumers. Paying special attention to the needs of underrepresented patient populations that traditionally lack access will allow a more inclusive patient care model. Cooperation between health IT, technology developers, third-party telehealth vendors, and the workforce is essential in building customized virtual platforms. Cycles of trials including analytics and feedback will produce successful iterations to produce a final, optimized workflow.
About Moha Desai
Moha Desai is a Principal of Healthcare Strategy and Transformation where she focuses on driving forward strategic, planning, financial, revenue cycle, operational improvement, and patient engagement healthcare projects for providers, federal government health agencies, and various firms requiring growth, business development, and project implementation and management. She has previously served in leadership roles at Partners HealthCare, Deloitte Consulting, Bearing Point, etc. Moha received her B.A. in Economics from Columbia University and her M.B.A. at Yale University.
References:
1. Bertakis KD, Azari R. Patient-centered care is associated with decreased health care utilization. J Am Board Fam Med. 2011;24(3):229-239. doi:10.3122/jabfm.2011.03.100170
2. Priorities in Focus – Person- and Family-Centered Care | Agency for Health Research and Quality. Accessed August 26, 2020. https://www.ahrq.gov/workingforquality/reports/priorities-in-focus/priorities-in-focus-person-family-centeredcare.html
3. Kuehn BM. Patient-Centered Care Model Demands Better Physician-Patient Communication. JAMA. 2012;307(5):441-442. doi:10.1001/jama.2012.46
The healthcare industry, like many others, has become a data driven industry collecting data from patients, doctors, labs, and payers that are all crucial to patient healthcare diagnosis and outcome. When it comes to research, data can be useful in creating emerging healthcare technological innovations, pharmaceutical discoveries, as well as other advances in the market but it also is accompanied by strong regulatory compliance, like stringent HIPAA laws, that dictate that this data is kept private and secure.
In fact, in 2019 there were a reported 418 HIPAA violations, which amounts to 34.9 million Americans who have been victims of healthcare data breaches, which is roughly 10% of the US population. And in 2020, there were 17 hospitals, health systems, and health plans that have settled for HIPAA violations.
The highest penalty for a HIPAA breach is $1.5 million per year and a violation can cost anywhere from $100-$50,000 depending on each case. That is why, the Bureau of Labor and Statistics estimates that positions for compliance officers will grow by over 8% from 2016 through 2026 because the need to keep healthcare information secure is critical and growing, especially in today’s COVID-19 environment.
Just this week, several health IT players have announced support for the recent HR 7988 bill that would mandate the Department of Health and Human Services to encourage “covered entities” to adopt best practices for complying with HIPAA laws, specifically in relation to security. And while security is a major concern – it goes hand-in-hand with data management. Understanding that data is secure, how to access data and following the data lineage from start to finish will help all healthcare organizations be HIPAA compliant.
Healthcare organizations understand how crucial data is; however, they have yet to understand the value of a robust, unified approach to data management that provides an organization with a single of view of metadata across all business intelligence systems used in the healthcare organization so that accessibility is simple, errors can be detected immediately, and data lineage can be traced in seconds. What also yet to be recognized is that data management is key in keeping in regulatory compliance with HIPAA. Unfortunately, many healthcare organizations are still operating on a manual system and if they are using systems with automated capabilities, they are often not unified – creating an enormous task of aggregating metadata manually which, by nature, is error-prone. Manual data management will lead to a wealth of HIPAA issues.
“This is an area both payers and providers have underinvested in and have remained behind compared to other industries, while their consumers have grown accustomed to data-driven experiences from other industries,” stated Munzoor Shaikh, senior director of healthcare and life sciences at West Monroe Partners, to TechTarget.
In order to create a unified, automated approach to metadata management and business intelligence, one that will keep the organization HIPAA compliant, three critical capabilities must be put in place by the BI & analytics teams – automated data lineage, automated data discovery and an automated business glossary.
1. Automated data lineage and discovery will enable the business intelligence team to locate an error in a report immediately, track the source of the data automatically, and fix that error quickly, much to the delight of the healthcare. Automated data lineage also allows BI teams to quickly and easily conduct impact analysis ahead of making a change to any ETL process as well as find the root-cause of a reporting error by tracing data back to its origin.
2. Another important capability is automated data discovery, which enables BI teams to locate the data scattered throughout their BI environment in seconds. Accessing accurate data efficiently will be heavily relied upon to make the most crucial business decisions and automating the process allows healthcare executives to easily create accurate reports so that decisions can be made fast. This is especially important at a time where constant, rapid changes are happening continuously and unpredictably.
3. A business glossary is a cornerstone of data management for healthcare executives operating in different locations and in different departments because it creates company-specific business terms and definitions so that there is a universal language for data input across every department within a corporation. This is an important step to organizing data since it assigns terminology to specific data, minimizing the use of different terms throughout various departments for the same data assets.
Creating a unified system for data across a healthcare organization will ultimately lead to better patient treatment and outcome, the unlimited potential for innovation, and efficiency that will impact executives, payers, and patients alike. However, compromised or misplaced data can put the patient and hospital at high risk.
About Amnon Drori
Amnon Drori is the CEO of Octopai, a provider of metadata management automation for BI & Analytics. Before co-founding Octopai he led sales efforts at companies like Panaya (Acquired by Infosys), Zend Technologies (Acquired by Rogue Wave Software), ModusNovo and Alvarion, and also served as the Chief Revenue Officer at CoolaData, a big data behavioral analytics platform.
There is no shortage of data in the healthcare industry. Unfortunately, more data doesn’t always mean better data – especially when it comes to patient information.
A unique patient identifier (UPI) is a method for standardizing patient identification. Individuals are assigned a unique code, and that code, rather than a Social Security Number, name, or address, is what is used by healthcare organizations to identify and manage patient information. A standardized code like this not only protects sensitive health information but supports the exchange of data between healthcare organizations and states as it is a number and format easily read and recognized by all.
While a UPI has yet to be nationally recognized and implemented, a foundation has certainly been made and the industry is perfectly poised to move forward.
The UPI helps healthcare organizations link the right records together, preventing duplicate records from being created. There are many ways duplicate accounts or variances can occur: address differences, name variations, maiden names and even user entry error.
With UPIs, providers and payers can link records together and have one complete record and view of the patient or member, ultimately leading to a better experience and increased patient safety. Without reliable records, patient safety takes a hit. Misidentification can contribute to incorrect treatments and adverse medication interactions that have had life-altering or fatal consequences.
The UPI’s ability to achieve accurate record match rates for every patient and member also improves efficient, patient-centered care coordination, as well as population health management strategies, prescription drug monitoring programs (PDMPs), social determinants of health and more.
It is important to note that the UPI is not a patient-facing number and is not known to the patient or provider. It does not collect or share any clinical claims or diagnostic information; its purpose is simply to link records together giving providers and payers a complete view of someone’s identity.
A healthcare organization’s ability to manage patient data is apparent from the beginning. An inadequate system can force patients to fill out forms they’ve already filled out multiple times or undergo duplicate tests as they travel between facilities. It can also confuse patients who have similar information, such as first and last names, during the identification process.
In an ecosystem built around a strong healthcare network, these discrepancies can be avoided. Patients are given a unique identifier that remains consistent across every healthcare facility they visit, from physicians’ offices to hospitals, pharmacies, specialists, long-term care facilities, and more. All providers that patients visit know exactly who they are.
And it isn’t just greater comfort and convenience that patients gain from a well-connected healthcare network. Managing patients and their data is vital for reducing medical errors. One Johns Hopkins study found that medical errors account for more than 250,000 deaths annually in the United States.
The challenge of managing patient data across the entire healthcare ecosystem isn’t new — interoperability has been a hot button issue for years now. While there are several master patient indexes that organizations can use to match patients with appropriate demographic data, these still include gaps, overlaps, and outdated patient information.
These indexes can’t keep up with simple things such as name and address changes or data entry errors. Therefore, providers and payers who rely on them have trouble matching their patients and members accurately. A more effective solution involves combining these data sets to create complete identities and profiles, where every piece of new data is instantly updated and verified.
For example, if a provider has a patient in their EHR twice under two spellings of the patient’s name in error, a UPI would link those two profiles, creating a singular view of the patient in that provider’s system.
Similarly, a UPI can help facilitate interoperability between healthcare providers. For example, if a pharmacy has a patient listed under a maiden name but the doctor has that same patient under a married name, the prescription during the ePrescribing process might not get associated with the right profile. If both organizations have the UPI on record and submit it during the transaction, the systems will match the patient using the UPI.
Every authorized care team member can immediately access the updated data related to a patient or member’s identity, which offers benefits far beyond treatment. For providers, this could mean increased collections. For payers, it could look like improved medication adherence.
For example, ValleyCare Health System in northern California was struggling with hundreds of bills being returned each month because of wrong patient addresses. When the health system implemented an identity verification program, it decreased the amount of returned mail by 90 percent.
Sorting through clinical data issues takes up a great deal of time. The administrative costs of healthcare account for nearly 8 percent of U.S. healthcare expenditures. By identifying patients through unique socioeconomic factors, healthcare organizations can more efficiently and accurately manage data and put it to good use.
A healthcare network tied together by streamlined data management provides an environment where duplicate or inaccurate information is detected and corrected almost immediately. Both patients and members are accurately identified, and their data retains its quality at every stage of care.
When combined with other patient engagement solutions, such as patient portals, data and identity management tools create the infrastructure needed for healthcare to truly become one cohesive, patient- and member-centric network.
Until recently, the use of federal funds for the adoption of a national patient identifier (NPI) was prohibited. The ban has limited the Department of Health and Human Services (HHS) from interacting with healthcare organizations to develop and implement an NPI strategy. In the years since the funding ban put the brakes on a universal approach, many disparate software solutions have been created which don’t talk or share information with each other. Integrating these systems in an industry of this scale will take a concerted effort.
The ban was lifted in July of 2020, giving HHS the ability to evaluate a full range of patient matching solutions and enable it to work with the private sector to identify a solution that is cost-effective, scalable, secure and one that protects patient privacy.
Karly Rowe, Vice President, Patient Access, Identity, and Care Management Products at Experian Health believes that “private-sector entities have already developed the technological foundation for data interoperability through the creation of UPIs that are maintained in a master person index.” These solutions are vendor neutral, meaning data can flow freely between disparate electronic health systems, regardless of size or location. With federal funding back on the table, “UPIs could be adopted with government oversight of private sector offerings and the creation of national standards to ensure quality patient matching and identification.”
At the end of 2019, Experian Health announced that every person in the United States (about 328 million Americans) had successfully been assigned a unique UPI, powered by Experian Health Universal Identity Manager (UIM) and NCPDP Standards
(the “UPI”).
Combining Experian’s expansive data assets and innovative UIM technology along with the unique ability NCPDP brings to share the UPI throughout the healthcare ecosystem using the NCPDP Telecommunication Standard and its SCRIPT Standard, each individual in the U.S. that has received medical care or utilized a pharmacy has been processed through the solution and assigned a UPI. As new patients enter the healthcare ecosystem, this number will continue to grow.
Having a single, unified and accurate view of patients and members is a challenge that plagues the healthcare system. Now, there is promise of a comprehensive solution that reduces the barriers to make healthcare safer.
Interested in learning more about unique patient identifiers and how Experian Health can help?
Download our eBook.
The post What is a Unique Patient Identifier? appeared first on Healthcare Blog.
Each year, Adverse Drug Events (ADE) account for nearly 700,000 emergency department visits and 100,000 hospitalizations in the US alone. Nearly 5 percent of hospitalized patients experience an ADE, making them one of the most common types of inpatient errors. What’s more, many of these instances are hard to discover because they are never reported. In fact, the median under-reporting rate in one meta-analysis of 37 studies was 94 percent. This is especially problematic given the negative consequences, which include significant pain, suffering, and premature death.
While healthcare providers and pharmaceutical companies conduct clinical trials to discover adverse reactions before selling their products, they are typically limited in numbers. This makes post-market drug safety monitoring essential to help discover ADE after the drugs are in use in medical settings. Fortunately, the advent of electronic health records (EHR) and natural language processing (NLP) solutions have made it possible to more effectively and accurately detect these prevalent adverse events, decreasing their likelihood and reducing their impact.
Not only is this important for patient safety, but also from a business standpoint. Pharmaceutical companies are legally required to report adverse events – whether they find out about them from patient phone calls, social media, sales conversations with doctors, reports from hospitals, or any other channel. As you can imagine, this would be a very manual and tedious task without the computing power of NLP – and likely an unintentionally inaccurate one, too.
The numbers reflect the importance of automated NLP technology, too: the global NLP in healthcare and life sciences market size is forecasted to grow from $1.5 billion in 2020 to $3.7 billion by 2025, more than doubling in the next five years. The adoption of prevalent cloud-based NLP solutions is a major growth factor here. In fact, 77 percent of respondents from a recent NLP survey indicated that they use at least one of the four major NLP cloud providers, Google is the most used. But, despite their popularity, respondents cited cost and accuracy as key challenges faced when using cloud-based solutions for NLP.
It goes without saying that accuracy is vital when it comes to matters as significant as predicting adverse reactions to medications, and data scientists agree. The same survey found that more than 40 percent of all respondents cited accuracy as the most important criteria they use to evaluate NLP solutions, and a quarter of respondents cited accuracy as the main criteria they used when evaluating NLP cloud services. Accuracy for domain-specific NLP problems (like healthcare) is a challenge for cloud providers, who only provide pre-trained models with limited training and tuning capabilities. This presents some big challenges for users for several reasons.
Human language very contexts- and domain-specific, making it especially painful when a model is trained for general uses of words but does not understand how to recognize or disambiguate terms-of-art for a specific domain. In this case, speech-to-text services for video transcripts from a DevOps conference might identify the word “doctor” for the name “Docker,” which degrades the accuracy of the technology. Such errors may be acceptable when applying AI to marketing or online gaming, but not for detecting ADEs.
In contrast, models have to be trained on medical terms and understand grammatical concepts, such as negation and conjunction. Take, for example, a patient saying, “I feel a bit drowsy with some blurred vision, but am having no gastric problems.” To be effective, models have to be able to relate the adverse events to the patient and specific medication that caused the aforementioned symptoms. This can be tricky because as the previous example sentence illustrates, the medication is not mentioned, so the model needs to correctly infer it from the paragraphs around it.
This gets even more complex, given the need for collecting ADE-related terms from various resources that are not composed in a structured manner. This could include a tweet, news story, transcripts or CRM notes of calls between a doctor and a pharmaceutical sales representative, or clinical trial reports. Mining large volumes of data from these sources have the power to expose serious or unknown consequences that can help detect these reactions. While there’s no one-size-fits-all solution for this, new enhancements in NLP capabilities are helping to improve this significantly.
Advances in areas such as Named Entity Recognition (NER) and Classification, specifically, are making it easier to achieve more timely and accurate results. ADE NER models enable data scientists to extract ADE and drug entities from a given text, and ADE classifiers are trained to automatically decide if a given sentence is, in fact, a description of an ADE. The combination of NER and classifier and the availability of pre-trained clinical pipeline for ADE tasks in NLP libraries can save users from building such models and pipelines from scratch, and put them into production immediately.
In some cases, the technology is pre-trained with tuned Clinical BioBERT embeddings, the most effective contextual language model in the clinical domain today. This makes these models more accurate than ever – improving on the latest state-of-the-art research results on standard benchmarks. ADE NER models can be trained on different embeddings, enabling users to customize the system based on the desired tradeoff between available compute power and accuracy. Solutions like this are now available in hundreds of pre-trained pipelines for multiple languages, enabling a global impact.
As we patiently await a vaccine for the deadly Coronavirus, there have been few times in history in which understanding drug reactions are more vital to global health than now. Using NLP to help monitor reactions to drug events is an effective way to identify and act on adverse reactions earlier, save healthcare organizations money, and ultimately make our healthcare system safer for patients and practitioners.
David Talby, Ph.D., MBA, is the CTO of John Snow Labs. He has spent his career making AI, big data, and data science solve real-world problems in healthcare, life science, and related fields. John Snow Labs is an award-winning AI and NLP company, accelerating progress in data science by providing state-of-the-art models, data, and platforms. Founded in 2015, it helps healthcare and life science companies build, deploy, and operate AI products and services.
Synthetic
chemistry has been with us for centuries, but it is now entering a new
frontier. Big data, AI and machine learning are unlocking a predictive power
that is transforming the conceptualization and optimization of synthetic routes.
A new white paper from the Entellect team titled “The foresight to bring scalability into chemistry modeling” looks at approaches to predictive chemistry modeling and considers how those models can be scaled to a sustainable pipeline that will be able to fully support pharmaceutical and chemical R&D.
The paper
explores topics like:
* The advantages
of leveraging predictive chemistry models
* How to tackle
the scale-up of predictive chemistry models, including prioritizing
collaboration, focusing on high-value activities and guaranteeing data security
* The importance
of quality data and the FAIR Data Principles
* Learning to evolve to meet the needs of end users
Interested in finding out more about scaling predictive reaction chemistry models? Read the white paper now.
What You Should Know:
– Holy Name Medical
Center based in New Jersey and Israel’s Sheba Medical Center, the largest
medical center in the Middle East announced a strategic partnership to develop
digital health and telehealth solutions, The
Times of Israel reports.
– As part of the partnership, Holy Name’s team will Sheba’s ARC (Accelerate, Redesign, Collaborate) Innovation
Center with the focus of identifying clinical needs and developing
solutions to medical challenges.
– ARC Innovation brings new technologies into the hospital
and community healthcare ecosystem to further improve patient care. It enables
data fluidity and integration amongst innovators; scientists; startups;
high-level developers; large and small companies; investors; and academia all
under one roof.
– ARC includes six medical tracks, with a senior Sheba
physician leading each: telemedicine, precision medicine, digital innovation
focusing on big data and artificial intelligence, augmented and virtual
reality, rehabilitation and surgical innovation.
– “Working in tandem with Sheba will enable us to participate in an open collaboration with world leaders in global healthcare innovation, all of us working together to find new and innovative ways to deliver patient care,” said Holy Name Medical Center, President & CEO Michael Maron in a statement.
– Healthcare Growth Partners’ (HGP) summary of Health IT/digital health mergers & acquisition (M&A) activity, and public company performance during the month of July 2020.
While a pandemic ravages the country, technology valuations are soaring. The Nasdaq hit an all-time high during the month of July, sailing through the 10,000 mark to post YTD gains of nearly 20%, representing a 56% increase off the low water mark on March 23. More notably, the Nasdaq has outperformed the S&P 500 (including the lift the S&P has received from FANMAG stocks – Facebook, Amazon, Netflix, Microsoft, Apple, Google) by nearly 20% YTD.
At HGP, we focus on private company transactions, but there is a close connection between public company and private company valuations. While the intuitive reaction is to feel that companies should be discounted due to COVID’s business disruption and associated economic hardships facing the country, the data and the markets tell a different story.
While technology is undoubtedly hot right now given the thesis that adoption and value will increase during these virtual times, the other more important factor lifting public markets is interest rates. According to July 19 research from Goldman Sachs,
“Importantly, it is the very low level of interest rates that justifies current valuations. The S&P 500 is within 4% of the all-time high it reached on February 19th, yet since that time the level of S&P 500 earnings expected in 2021 has been pushed forward to 2022. The decline in interest rates bridges that gap.”
Additionally, Goldman Sachs analysts also estimate that equities will deliver an annual return of 6% over the next 10-years, lower than the long-term return of 8%. Future value has been priced into present value, and returns are diminished because the relative return over interest rates is what ultimately matters, not the absolute return. In short, equity valuations are high because interest rates are low.
What happens in public equities usually finds its way into private equity. To note, multiple large private health IT companies including WellSky, QGenda, and Edifecs, have achieved 20x+ EBITDA transactions based on this same phenomenon. From the perspective of HGP, this should also translate to higher valuations for private companies at the lower end of the market. As investors across all asset classes experience reduced returns requirements due to low interest rates, present values increase across both investment and M&A transactions.
As with everything in the COVID environment, it is difficult to make predictions with certainty. Because the stimulus has caused US debt as a percentage of GDP to explode, there is an extremely strong motivation to keep long-term interest rates low. For this reason, we believe interest rates will remain low for the foreseeable future. Time will tell whether this is sustainable, but early indications are positive.
Noteworthy M&A transactions during the month include:
Noteworthy Buyout transactions during the month include:
Noteworthy Investments during the month include:
HGP tracks stock indices for publicly traded health IT companies within four different sectors – Health IT, Payers, Healthcare Services, and Health IT & Payer Services. Notably, primary care provider Oak Street Health filed for an IPO, offering 15.6 million shares at a target price of $21/ share. The chart below summarizes the performance of these sectors compared to the S&P 500 for the month of July:
The following table includes summary statistics on the four sectors tracked by HGP for July 2020:
About Healthcare Growth Partners (HGP)
Healthcare Growth Partners (HGP) is a Houston, TX-based Investment Banking & Strategic Advisory firm exclusively focused on the transformational Health IT market. The firm provides Sell-Side Advisory, Buy-Side Advisory, Capital Advisory, and Pre-Transaction Growth Strategy services, functioning as the exclusive investment banking advisor to over 100 health IT transactions representing over $2 billion in value since 2007.
The European data strategy aims to construct common data spaces for all, create a single EU market for data, and catalyze a dynamic data economy. In a previous post, we briefly described the essence of the envisioned heath data space and pointed at opportunities and possible starting points to transform this vision into reality.
However, several questions have also emerged in exchanges with
thought leaders and collaborators. Borne from gray areas around data, systems
and mechanisms for common use, these questions serve as food for thought
to structure an open, cross-sector discussion.
What data
We need to talk honestly about data quality and data bias. In our
experience with AI, efforts to make data reliable, transparent and reusable go
a long way in catalyzing new, machine-based data exploration. Part of that
effort is to define the amount and type of bias permissible in data for a
particular purpose because there are no data without bias.
Which system
Vast amounts of data on biology, chemistry and health need to be
machine-readable on a massive scale. Manual curation is not an option, but in a
framework that spans from big data in populations to patient-centric care –
where data are personal and private by definition – how do we verify the
fidelity of automated processing and build trust in the system?
Where to guide
Central guidance is essential for unified empowerment, but
individuals should participate in a common data space because they understand
the benefits and not just because there are safeguards against risks. Robust
but flexible guidance can address seemingly insurmountable differences among
sectors in the way data are generated and valued, while ensuring that abuses
are not rewarded. We need to understand the interests and objectives of all
parties. Only so can these spaces be truly inclusionary.
It is time for a dialogue – time to openly define the needs,
interests, objectives and differences of participants in an EU health data
space. We want to hear your thoughts; not only about the nature of these data
spaces but also about the forum in which clinical researchers and data
scientists can access unified research, literature and clinical data in one
secure environment. To join the discussion, please email my colleague Xuanyan
Xu at [email protected].
