Showing posts with label healthcare data. Show all posts
Showing posts with label healthcare data. Show all posts

Friday, August 30, 2024

Why aren’t we using the data we know we have to improve patient outcomes?

“Even internally, collaborating [on data] can be tough with privacy and protection requirements.” — Alexander Gusev, PhD

Efficient and scalable data flows are critical to every industry, and health care is no different. Data are one of the biggest challenges in improving patient outcomes in health care. The efficiency and scalability of any data flow is impacted by data security, privacy, governance and the overall maturity of data operations of those involved. Of course, personal health information is a protected data type, and the flow of these data is neither efficient nor scalable due to the bulky and manual processes required to satisfy security and privacy requirements. To simplify the conversion, we’ll divide data flows into two categories: direct data flows when providing direct health care services and indirect data flows to research groups identifying breakthroughs to be used by care providers in delivering better services.


Direct care: Sharing data between facilities


Direct care is when a care provider needs data from another care provider to treat a common patient.

Let’s say a patient has a heart monitor installed at a provider in California. While traveling in New York, this patient experiences chest pains and goes to the emergency room, as instructed. The facility in New York will most likely have to manually reach out to the California facility to coordinate access to the data. In some cases, it may be faster for the New York facility to rerun the same tests rather than wait for the process required for data sharing to occur.

Many facilities transfer sensitive data via fax, and requests are manual rather than self-service. Delays or failures in getting answers to critical questions can reduce the quality of patient outcomes.


Direct care: Delivering living health plans and care


CVS and other notable health care giants have been working toward a goal of a new type of living health care. The idea is that we can keep people healthier for longer if patients engage with health professionals before issues become problems. This type of service requires access to a wide range of personal and health data that we have but are strewn across various services, organizations and infrastructures: lab tests, genomic data, Internet of Things devices such as wearables, sleep trackers, scales and more. To accomplish such a goal, these health giants must figure out how to efficiently store, share and use these highly sensitive data while meeting compliance requirements.

Advanced models will be used to process all these data to come up with care instructions to improve the lifespan and health of patients. Living health plans could include preventive lifestyle changes such as a focus on high-quality sleep, which requires attention to gut health, diet, exercise and mental health. Living health would also benefit by incorporating recent breakthroughs in early detection of issues as we see with the study of pathogens, cancers and diseases at a genomic level as studied at Dana-Farber Cancer Institute, the Atlanta Genome Project and others. The problem at every step is not that the data do not exist but that they are very difficult to gain access to. This access typically takes time and requires processes like deidentification that reduce data quality and, therefore, insight quality and accuracy.


Indirect care: Discovering medical breakthroughs to improve patient outcomes


Indirect care is when medical researchers, pharmaceuticals and more need patient data (often, real-world data) to improve everything from early detection to treatments and cures. Naturally, care providers benefit from and want to support these breakthroughs but are challenged by the time, resources and regulations involved in such data sharing. In addition, requirements around data sharing for indirect care are far more stringent than for direct.

Essentially, improving areas like disease detection, prevention and treatment requires that care providers share data from real-world patients (as opposed to screened clinical trial participants); this is called real-world data (RWD). RWD must be analyzed for researchers and pharmaceutical organizations to discover medical improvements and breakthroughs that can then be passed to the care providers.

In talking with various researchers, the common challenge isn’t the value of what will be done with the data but getting care providers to trust that we can and will protect the data.


Improving patient outcomes with PETs-enabled solutions


There is a segment of technologies called privacy enhancing technologies (PETs). Such technologies span advanced cryptographic methods as well as hardware solutions. The various PETs are not in competition with each other but serve different purposes. In many cases, combining multiple PETs is the solution. Raw technologies themselves require deep expertise and time to leverage. The health care ecosystem needs a solution that operationalizes these technologies in a software layer that institutions can easily deploy and use.

“Now, [with PETs-enabled solutions] we can start recruiting other collaborators to show that it can work in a plug-and-play way for these hospitals…that will lead us to participation from far more institutions,” says Alexander Gusev, PhD, lead researcher at Dana-Farber Cancer Institute.

For direct care, PETs-enabled solutions allow providers to obtain information about patients in a secure and more self-service way. Another example would be to address the difficulty in using and sharing unstructured data: e.g., MRIs, X-rays, doctors’ notes fields. With PETs-enabled workflows, doctors at any facility will submit an MRI to an artificial intelligence model trained on tens of thousands of MRIs from many facilities to quickly receive a diagnosis based on the most recent medical discoveries. This could require a combination of PETs such as confidential computing infrastructure and federated learning to create a confidential federated learning flow to support the many disparate data sources while protecting both the model and data while these insights are generated.

Providers can use this new confidential federated learning for indirect care to train new models across data sets from multiple medical centers. This approach optimizes models for disease identification (e.g., cancer identification) while ensuring that data never leave their premises, thus remaining protected. This combination of PETs protects the model weights sent to the global server for aggregation, providing a robust, secure solution.

Every industry in the world is data-driven. The speed and scale of data operations must not sacrifice security or privacy. Traditional methods of using data leave companies vulnerable to breaches, like those experienced by UnitedHealth, 23andMe and many more. These breaches give pause to people being asked for consent for additional use and distribution of their data. PETs are specifically designed to protect data while in use, which not only closes major vulnerabilities and allows people to confidently provide consent but accelerates the work we already do while unlocking the value of data not previously accessible.

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Sunday, January 9, 2022

Eliminating essential gaps in care starts with data

It took 27 weeks for physician visits to return to pre-pandemic levels after COVID-19 emerged, and some specialties, such as gastroenterology, nephrology and urology, have been slower to rebound than others, an MGMA analysis found. This raises the risk that outcomes for complex conditions could worsen due to delayed care, especially among populations that struggled to access care before the pandemic.

Now, leading practices are exploring the use of data to detect complex conditions sooner, identify treatment options faster and empower patients to be more engaged in their care.

How can healthcare organizations lean on data to eliminate essential gaps in care and improve health outcomes for vulnerable populations? Here are four approaches to consider.

No. 1: Look for trends among missed and canceled appointments. For example, the MGMA analysis revealed that seniors were likely to cancel preventive screenings due to fears of contracting the COVID-19 virus in a healthcare setting. A deeper dive into the data showed that missed screenings were especially prevalent for chronic kidney disease and prostate cancer. With data such as this in hand, physician practices and health systems can devise creative ways to encourage individuals to get caught up on recommended screenings, such as through mobile or pop-up clinics, early detection kits sent by mail, or in-home laboratory services or clinician visits. Data-driven efforts to support early detection of disease can make a profound impact on health outcomes. Prostate cancer, for example, has an extremely high one-year survival rate—around 100%—when it is detected in stages 1, 2 or 3, but the survival rate falls to 87.6% when it is detected in stage 4. This is an instance where early detection can lead to longer survival.


No. 2: Make it easy for medical assistants to ask questions that inform discussions at the point of care. An American Medical Association expert estimates physicians could reduce administrative tasks by up to three hours a day by more effectively leveraging medical assistants. One urology practice in San Diego, Genesis Urology, uses innovative technologies that prompt medical assistants to ask relevant, specific questions that help identify risk for disease or—for patients with a history of disease or chronic illness—ascertain where patients are in their care journey. This safely delegates data gathering to non-providers. It also gives physicians a comprehensive view of the patient’s medical history at a glance, supporting deeper, more nuanced conversations that inform medical decision-making and in-office follow-up. With this approach, the practice identified new treatment options for 45 patients with prostate cancer.

No. 3: Automate clinical workflows for more timely data capture and proactive follow-up. When physicians do not have the data they need at the point of care, such as lab test results or imaging scans, this diminishes patient trust at a time when some individuals already are fearful of in-office care. It also increases operating expense for specialty practices, which must engage in extra work following the appointment to track down missing information and reengage patients. By using automated tools to detect information gaps and prompt clinicians and assistants to obtain this data prior to the visit, specialty practices can eliminate “wasted visits.” Further, artificial intelligence-based solutions can prompt patient navigators—health professionals who help facilitate access to care, including for individuals with complex care needs—to proactively check in with patients who have delayed screenings or follow-up visits during the pandemic. It’s an approach that supports earlier detection of disease and better health outcomes. At Genesis Urology, when the practice used AI to prompt proactive follow-up with patients based on risk factors such as delayed screenings and diagnostics, 20% of the patients who responded were found to have progression of their prostate cancer.

No. 4: Explore data-driven care coordination. Studies have shown that the use of patient navigators to assist patients with chronic conditions reduces costs and improves outcomes. When patient navigation support is paired with data-driven navigational workflows, navigators receive automatic nudges to close gaps in data, such as by scheduling lab tests or imaging scans, uploading results and ensuring that physicians receive the information. For Genesis Urology, use of data-driven workflows for care navigators has improved symptoms of disease among prostate cancer patients and reduced side effects while enhancing patient satisfaction. It has also strengthened the practice’s bottom line, increasing Chronic Care Management (CCM) revenue by $12,000 per month.

A Data-Smart Approach to Post-COVID Specialty Care


At a time when two out of three individuals are not receiving recommended cancer screenings and more than half are skipping or delaying routine care, patients’ risk for serious health issues, including disease progression, is rising—and specialty practices need to be prepared. By taking a data-driven approach to detecting chronic or complex conditions, identifying treatment options and engaging individuals in their care, specialty practices can enhance patient outcomes. They can also help avoid bottlenecks in care while strengthening revenue. It’s a data-smart approach that ensures the right resources are used in the right way, creating a better experience for patients as well as physicians.


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Sunday, November 7, 2021

Three steps to fixing the data quality issue plaguing the U.S. health care system

Data quality is essential to improving the U.S. health care system. But what is data quality? Basically, data quality is about extracting value for patients, clinicians and payers. High-quality data is both usable and actionable. In contrast, low-quality data, such as duplicate records, missing patient names, or obsolete information, creates barriers to care delivery and billing/payment while costing money across the health care system through inefficiency.

Unfortunately, health care has lagged virtually every other industry in leveraging data for strategic and operational advantage. This is not for lack of data. More patient health data than ever is being captured today by medical equipment, digital devices and apps. Indeed, if data is gold, health care is sitting on top of a goldmine.

Yet the industry generally has done a poor job of mining and refining this gold ore of data to a point where it can be useful. That is a wasted opportunity because refined, quality data would provide value to all health care stakeholders—hospitals, payers, health information exchanges, labs, and patients.


Impact on research, patient outcomes


Poor data quality has negative ramifications throughout health care. The way we bring new medications to market is a great case in point. The first phase is identifying the medication, a process that comprises less than 20% of the cost of drug development. Next comes clinical trials, which account for the vast majority of drug development costs.

Clinical trials are long and arduous. Worse, from a data quality standpoint, data still are collected on paper and then transferred to computer, and typically not associated with other data sets for similar types of patients or even other patients in that clinical trial. If all this information were easily available and everything was automated, clinical trials would cost less than 10% of what they do now.

I just started participating in a clinical trial and it is amazing how much effort is required just to log in to an older system for a single trial. There may be six to 12 different logins and each requires special access permissions. It can take several days to log in to just one of these systems. All that information under these logins should be united to become quality, actionable information that's easily available.

Even in the physician's office there's a significant lack of aggregated data for patients, leaving clinicians knowing only part of a patient’s story. And when a doctor sees a patient at the hospital, that doctor needs the patient’s ambulatory outpatient records to make sound, evidence-based decisions about appropriate treatment. In far too many cases, however, the data that would inform clinicians at the point of care are trapped in siloes scattered across the health care landscape.


Healthcare has not availed itself of advanced digital technologies such as artificial intelligence (AI) and machine learning (ML), which are being used to transform many other industries. In large part this is because its either not accessible or the quality of health data is so poor that intelligent machines would struggle to process and analyze it for actionable insights into patient care.

In contrast, quality health care data creates scenarios in which AI/ML quickly can provide clinicians at the point of care with information that enables them to educate patients about their specific condition, offer referrals to appropriate specialists, suggest new medications, and improve outcomes. With the right data and insights, clinicians also might be able to match the patient with a clinical trial that could save the patient’s life.

Low-quality data is a problem for payers because they need data to make decisions, whatever it’s condition. It starts when the patient gets a diagnosis and the payer may not be aware of it for weeks. So a patient diagnosed with cancer might suddenly have to find a specialist and determine whether the practice takes their insurance.

But if a payer finds out right away that this patient may have cancer, the payer can help guide patients to the right provider, removing a heavy burden of financial concerns and medical management.


Three steps to quality data


There are three main steps to achieving high data quality. The first is ensuring access to the data the clinician needs. There are still many legacy systems in health care where the vast majority of medical records are housed within an organization’s servers and not on a cloud, where they could be more easily accessed and aggregated by authorized users. While health care interoperability and data sharing have improved in recent years, we’re still building integrations one at a time. Bringing data into a central database where it can be turned into gold remains a challenge.

The next step involves identity management. Much of the value in health data comes from its ability to document longitudinal change in individual patients. Clinicians may, for example, want to see how medications or disease processes affect lab results for a particular patient. Even if they could connect disparate data sources and have the data go into a single database, they can't associate the data with a specific patient. With effective identity management, though, it is possible to create a good longitudinal patient record that can bring huge clinical and efficiency benefits.

The third phase of data quality will finally make the data usable or actionable. Once the data is in the right patient's chart and created a longitudinal record, it must be organized and easy for clinicians to locate and read. This requires a process called data normalization. Health data can come from multiple sources (EHRs, labs, pharmacy systems, etc.), all of which may use different coding for a medical procedure, different terms for a certain test, or even use different language to categorize genders. Data normalization creates a common terminology that enables the semantic interoperability necessary to make data actionable.

There’s no magic bullet for improving health care data quality. It will require a joint effort and the innovation inherent in the free market. There will be companies that will help us collect and connect to data and there will be companies and entities that will help us connect to data. There will be technologies that will identify the data and others that will normalize the data so it looks similar—and becomes actionable—no matter where it originated. And there will be companies that provide quality databases that health care stakeholders can use to perform advanced analytics. The end result will be a health care system that provides better care at a lower cost.


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