Digital Disease Management to optimize care and improve patient outcomes

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How AI-Driven Healthcare Data Management Impacts the Future of Healthcare

March 24, 2020

Machine Learning and AI Transform Healthcare Data Management so Healthcare Stakeholders Can Achieve Lower Costs and Better Patient Outcomes

Healthcare is a rapidly changing industry. Big data, artificial intelligence (AI), and machine learning – these terms have become mainstream in many industries and healthcare is no exception. AI applications are increasingly becoming a part of healthcare to the point where there have been rumblings about AI possibly taking over some healthcare jobs. But is this realistic? Should healthcare stakeholders all just embrace AI in healthcare?

What is Artificial Intelligence (AI)?

Artificial intelligence is a collective term used in reference to multiple technologies that aim to mimic human cognitive functions, making machines able to sense, understand, act, and learn. Some AI technologies that are important to healthcare stakeholders and the healthcare industry are:

  • Machine learning – neural networks and deep learning: statistical techniques used to fit models to data and to ‘learn’ by training models with data.
  • Natural language processing (NLP): making sense of human language with applications such as speech recognition, text analysis, and translation.
  • Rule-based expert systems: based on “if-then” rules.
  • Physical robots: actual robots such as surgical robots.
  • Robotic process automation: computer programs used to perform structured digital tasks for administrative purposes.

AI in Healthcare – Past, Present, Future

Healthcare is a data rich industry which provides fertile ground for applications of AI in healthcare data management as well as other aspects of healthcare. In the past, AI technologies in healthcare were mainly algorithms or tools that complement a human. The rule-based expert systems of “if-then” rules were dominant in the 1980’s and for some time beyond. However, inefficiencies in such systems that result when the number of rules become too large, are causing them to be replaced by machine learning algorithms.


Today, AI in healthcare can truly augment human activity and is taking over tasks ranging from medical imaging to risk analysis to diagnosing health conditions. AI is being used to discover links between genetic codes, to power surgical robots, and to maximize hospital efficiency. As a result of this, growth of AI in healthcare is exploding and is estimated to reach $6.6 billion by 2021. An analysis of the market found that when combined, key clinical health AI applications can potentially create $150 billion in annual savings for the United States healthcare economy by 2026.


In the future, AI will play a critical role in precision medicine, which is the direction in which healthcare delivery is headed. AI is expected to improve areas such as diagnosis and providing treatment recommendations, imaging analysis, healthcare data management, patient communication, and capturing of clinical notes through speech and text recognition.

According to a PWC report, Why AI and Robotics will Define New Health, AI in past decades have focused on innovations in medical products (equipment, hardware, and software) that deliver historic and evidence-based care. The present decade has seen a rise in medical platforms focused on real-time, outcome-based care in the form of wearables, big data, healthcare data management, and health analytics. Healthcare stakeholders should be aware that it is believed that the next decade will see an increase in medical solutions involving robotics and augmented reality, which will deliver intelligent solutions for both evidence and outcome-based health and focus on collaborative, preventative care.

Uses of AI in Healthcare

“AI is being used to discover links between genetic codes, to power surgical robots, and to maximize hospital efficiency.”

One description of AI is that it “simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost.” AI technologies have found a multitude of uses in healthcare such as:

  • Efficiently and accurately diagnosing conditions and reducing errors
  • Treatment recommendations
  • Development of medicines
  • Streamlining the patient experience
  • Data mining and healthcare data management
  • Robot-assisted surgery
  • Administrative applications

With all these uses and more, AI in healthcare is here to stay and healthcare stakeholders who want to remain relevant and thrive in this space, need to make the necessary moves now to incorporate AI into their operations and optimize healthcare data management.

Impact of AI on Healthcare Data Management and Healthcare Stakeholders

The amount of data available in healthcare is staggering. Data sources include electronic medical records (EMRs), insurance claims, clinical trials, drug research and development, and patient generated health data. All these points of data have the potential to change the healthcare landscape if properly managed and leveraged accordingly. However, without proper healthcare data management, valuable data can become lost among the large volumes of data points available; this is where AI can help healthcare stakeholders.

“Healthcare could save up to $100 billion a year by utilizing big-data crunching algorithms backed by AI to inform decision-making and realize efficiencies in clinical trials and research”


It is estimated that the industry could save up to $100 billion a year by utilizing big-data crunching algorithms backed by AI to inform decision-making and realize efficiencies in clinical trials and research. There are companies that are leveraging the power of AI to assist healthcare stakeholders with healthcare data management and improving healthcare delivery. Tempus is one company that is using AI tools to collect and analyze the world’s largest library of clinical and molecular data to drive precision medicine. Its AI-driven data are being used in cancer research and treatment.


KenSci is another company that is using the power of AI and big data to improve healthcare. Their risk reduction platform aggregates data from existing sources such as EMRs, claims, and financial data to help uncover clinical, operational, and financial risks. It can predict who might get sick and the drivers behind healthcare costs; it can also provide solutions to these problems.


H2O.ai uses AI to analyze data throughout a healthcare system to mine, automate, and predict processes. It has been used to foresee ICU transfers, improve clinical workflows, and even pinpoint a patient’s risk of hospital-acquired infections.


AI-enabled applications are also impacting healthcare data management practices. Computer-assisted coding (CAC) is on the rise, utilizing NLP to read and interpret clinical documentation in patient health records and suggest applicable diagnosis and procedure codes. For CAC to be fully adopted and optimal efficiencies realized, the medical coding workflow will have to be re-engineered. As machine learning becomes more integral to reading images for diagnosis, the requirement for a physician to interpret an image may become less necessary. As such, medical coding and reporting guidelines and standards will need to be adjusted to account for AI applications.

Benefits of AI in Healthcare

AI has proven to be a boon to the healthcare industry. Some of the benefits from incorporating AI technologies in healthcare include:

  • Faster, earlier, and more accurate diagnoses
  • More efficient data mining and healthcare data management
  • Lowered costs
  • Improved patient outcomes
  • Enhanced patient engagement
  • Healthier behaviors and proactive lifestyle management with wearable technology
  • Better understanding of the patient’s condition and improved management resulting from increased insight of the healthcare team into the day to day lives patients
  • Improved clinical decision-making
  • Reduced administrative burdens and improved efficiency in managing administrative tasks
  • More efficient drug research and discovery process potentially cutting both the time to market for new drugs and their costs significantly

AI technologies are already proving to be a game-changer in healthcare and there is still a huge potential for them to do even more. Many companies including big names such as IBM and Google are investing heavily in AI for healthcare. We are on the cusp of many discoveries and breakthroughs as the potential of AI is fully realized in the healthcare industry.

Challenges/Pitfalls of AI in Healthcare

While the advancement of AI technologies in healthcare is exciting and offers numerous benefits, like everything else, it is not without challenges. Some healthcare stakeholders prefer to take a cautious approach, with some wondering how far is too far. Below are some of the challenges/pitfalls facing healthcare stakeholders and the evolution of AI in healthcare:

  • Potential biases in results based on the data used to create algorithms and ‘train’ the AI technology.
  • Transferability of algorithms ‘trained’ in one setting or population to another.
  • Data ownership, confidentiality, and consent – who owns the data? Who is responsible for healthcare data management? How will patients’ data be kept confidential? How do healthcare stakeholders handle the need for consent in research?
  • Ethical issues and professional responsibility – who is responsible when a patient is misdiagnosed?
  • Legal risks and regulatory issues – at present, regulations are falling behind the explosion in AI development and use potentially creating a legal and regulatory nightmare for healthcare stakeholders.

Healthcare is experiencing an upsurge in the development and application of AI technologies. These technologies are very beneficial in that they can perform tasks that are usually done by humans in less time and at much lower costs, simplifying the lives of healthcare stakeholders including patients, doctors, and hospital administrators. Healthcare data management is one area that has benefited significantly from AI and has resulted in better patient outcomes and cost savings for healthcare stakeholders. However, there are some serious challenges to the use of AI in healthcare that need to be overcome. While AI might not take over healthcare jobs, these challenges need to be managed before we can all completely embrace the full potential of AI in healthcare.

To find out more about how wearables and patient generated health data are part of the bigger AI picture in healthcare, contact the healthcare technology experts at Acuma Health.


Understanding Medication Adverse Effects and How Healthcare Data Management Can Provide Proactive Prevention

Understanding Medication Adverse Effects and How Healthcare Data Management Can Provide Proactive Prevention

March 10, 2020

Adverse Drug Reactions vs. Adverse Drug Events – Detect and Avoid for Patient Safety

We are experiencing a health craze. Everybody is becoming health conscious and there is an abundance of herbs, supplements, lotions, etc. that people are using in their quest to rid themselves of, and prevent, ailments.

In addition, there are numerous prescription medications (almost 6,800) and countless over-the-counter drugs available in the US market. The use of so many substances in health care opens the door to possible interactions between substances, resulting in medication adverse effects. In the clinical space, terms such as adverse drug events (ADEs) and adverse drug reactions (ADRs) are used to describe some of the possible medication adverse effects that can result from drug use. But does the average person understand the meaning of these terms?

An adverse drug event (ADE) is defined as “an injury resulting from medical intervention related to a drug.” An ADE results from harms caused directly by the drug itself and include medication errors, ADRs, overdoses, and allergic reactions. An ADR, on the other hand, is a harmful and unintended response to a drug at normal doses and during normal use.

The Office of Disease Prevention and Health Promotion, part of the US Department of Health and Human Services, has named ADE prevention as an important patient safety priority in its National Action Plan for Adverse Drug Event Reporting. It noted that ADEs accounted for an estimated one-third of hospital adverse events and approximately 280,000 hospital admissions annually. The ADE Action Plan was established to coordinate multiple stakeholders and align Federal efforts in identifying common, preventable, and measurable ADEs that may result in significant patient harm. The goal is to jointly work towards reducing patient harm from these specific identified ADEs nationally.

Three types of ADEs were selected as the high-priority targets of the ADE Action Plan as they were identified as being common, clinically significant, preventable, and measurable. These are:

  • Anticoagulants: primary ADE of concern – bleeding
  • Diabetes agents: primary ADE of concern – hypoglycemia
  • Opioids: primary ADE of concern – accidental overdoses/over-sedation/respiratory depression

The World Health Organization lists has reported that as many as 4 in 10 patients globally are harmed in primary and outpatient health care. Up to 80% of harm is preventable. The most detrimental errors are related to diagnosis, prescription and the use of medicines. As such, healthcare organizations are urged to utilize the ADE Action Plan and implement strategies to prevent medication adverse effects, especially from the three priority types of ADEs identified. Healthcare data management and healthcare technology can play a major role in this regard, and those organizations that proactively use healthcare technology to detect ADEs will be a step ahead in preventing them. Some ways in which healthcare data management and healthcare technology can be used in ADE detection and prevention include:

  • Electronic exchange of health information, such as laboratory results and care (e.g., discharge) summaries. This can help to improve communication among the care team as a patient passes from one team to the next.
  • Interoperability between laboratory and pharmacy systems to help prevent medication errors and medication adverse effects.
  • Utilize electronic health records (EHRs) and patient engagement tools to provide patient-specific data that can inform appropriate clinical decisions by providers. EHRs and patient engagement tools can also provide clinical reminders and templates to prompt and facilitate recommended clinical practices. This could result in improvements in assessment, documentation, and collaborative treatment planning for patient risk factors and aberrant behaviors.
  • Leverage EHR Meaningful Use requirements by incorporating Quality Measures specific to the three types of ADEs identified in the ADE Action Plan.

ADEs can also be prevented by improving patient compliance with their medication regimen. Incorporating remote monitoring solutions such as automated medication dispensers with reminders can improve patient compliance and also prevent potential overdoses especially in seniors with memory problems.

Patient safety is a priority for all healthcare organizations and medication adverse effects are a serious threat to this. By proactively incorporating healthcare technology to detect and avoid ADEs, organizations can increasingly prevent them and improve patient safety. Acuma Health can show you how to utilize technology for proactive prevention of medication adverse effects and improved patient safety.


PGHD Patient Generated Health Data Acuma Health

Patient Generated Health Data (PGHD) Management to Improve Patient Care

February 18, 2020

Optimal Healthcare Data Management Can Achieve Benefits for Patients, Clinicians, and Payers

Patient generated health data (PGHD) is everywhere. There are many healthcare technology apps and devices that assist in patient data collection. Patients are eagerly collecting, tracking, and storing health data such as activity levels, and some are sharing that data with their healthcare providers. Some healthcare providers are utilizing patient data collection to aid in their decision-making to improve patient care. PGHD has the potential to change the healthcare landscape and healthcare stakeholders can leverage PGHD to improve outcomes and reduce costs.

In order to optimally leverage patient generated health data to produce useful insights and improve patient care however, proper patient data collection management is critical as data collected but not properly managed can become useless. Healthcare data is sensitive, personal, and protected by regulations such as the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule and, as such, healthcare data management can be an intricate process.

When looking to capture, store, and utilize PGHD, healthcare stakeholders need to consider factors such as data privacy and security, data accuracy, data governance, interoperability, and compliance with regulations. Because of this, the process requires careful thought and planning and development of proper PGHD frameworks to ensure optimal healthcare data management and utilization.

What is Healthcare Data Management and Why is it Important?

In a broad sense, healthcare data management is the process of storing, protecting, and analyzing data pulled from diverse sources. Healthcare stakeholders are swamped with patient data collection activities from myriad sources including electronic health records (EHR), electronic medical records (EMR), and of course, PGHD. All these data sources must be effectively managed if the power in the data is to be successfully harnessed and utilized.

“Managing the wealth of available healthcare data allows health systems to create holistic views of patients, personalize treatments, improve communication, and enhance health outcomes.”

Evariant

Optimal healthcare data management,especially with the incorporation of patient generated health data, is important as it provides powerful insights into the patient life story beyond the walls of the medical establishment. This will enable the healthcare provider to better personalize the care offered to patients, improving patient outcomes and reducing costs associated with hospital admissions.

Healthcare data management through healthcare technology and data analytics is invaluable to population health management and precision medicine, driving research and improving health.

Leveraging healthcare technology for best-in-class healthcare data management is also critical if healthcare stakeholders are to remain compliant with regulations. In the era of value-based payments and meaningful use , healthcare data management must be a top priority for all healthcare stakeholders looking to remain relevant and functional in the healthcare space.

Benefits of Optimally Managing Patient Generated Health Data

There are many benefits to be gained from the optimal management of patient data collection and PGHD . There are benefits for a variety of healthcare stakeholders including patients, clinicians, payers, and researchers.

Advantages of Improved Patient Data Collection for Patients

Patients set to benefit from the optimal management of patient generated health data through ways such as:

  • More involvement in their personal healthcare as they take ownership of collecting and sharing PGHD with providers
  • Better engagement and communication with providers
  • Enhanced understanding of health conditions
  • Improved management of health conditions
  • Fewer hospitalizations and reduction in associated costs

Benefits of Optimized Healthcare Data Management for Clinicians

When PGHD is collected, shared with the provider and optimally managed through the use of healthcare technology, clinicians can realize many benefits. These include:

  • A more complete view of the patient’s quality of life over time and beyond the healthcare setting
  • Deeper insight into the patient’s adherence to treatment plans including medication adherence
  • Ability to note trends and intervene in a timely manner before acute episodes of illnesses
  • Better treatment outcomes and reduced hospitalizations
  • Increased patient engagement
  • Better patient retention

Benefits of Well-Managed Patient Data Collection and Management for Payers

Payers in the healthcare space can also benefit from optimal patient data collection and management.  Benefits to payers include:

  • Obtaining value for money by tying reimbursements to shared decision-making between providers and patients through incorporation of PGHD in care decisions
  • Offer incentives for the use of PGHD by providers

Patient Generated Health Data Management Benefits for Researchers

Patient generated health data when properly managed with healthcare technology, can provide a treasure trove of valuable information for researchers helping them to:

  • Conduct comparative effectiveness research to assess medical therapies to determine the best and most cost-effective therapeutic solutions for routine clinical use
  • Advance the field of personalized medicine
  • Develop predictive modelling and analytics
  • Make progress in the field of population health management
  • Monitor patients who are participating in clinical trials

Best Practices in Managing Healthcare Data with Healthcare Technology

Proper healthcare data management can be a daunting task. With the volumes of data that consistently flow into the healthcare system, added to the emerging field of PGHD, healthcare data management can overwhelm even the most seasoned healthcare professional if not done properly. However, the many benefits to be gained by different healthcare stakeholders from optimal healthcare data management, are enough to make the effort worthwhile.

Successfully managing healthcare data with healthcare technology to achieve the greatest benefits involves implementing measurement systems, as was executed in several healthcare case studies.  These measurement systems are designed on certain principles, such as: 

  • fitting the PGHD into the flow of care and using the data to make it easier for clinicians to do their jobs and for patients to engage in self-management and make informed decisions
  • ensuring the PGHD measurement system is co-designed with healthcare stakeholders engagement
  • engaging with patients and clinicians about how to use the PGHD
  • merging PGHD with data from other sources (clinician reports, medical records, claims) for optimal utility of the data
  • continuously improving the PGHD measurement system based on the experiences of users and new healthcare technology

Some healthcare stakeholders have begun optimizing patient generated health data, utilizing it to generate actionable insights and guide decision-making. A 2015 survey among healthcare executives found that 73% reported a positive return on investment (ROI) in healthcare technology  involving PGHD such as wearables that track fitness and vital signs.

“The use of such Digital Health apps in just five patient populations where they have proven reductions in acute care utilization (diabetes prevention, diabetes, asthma, cardiac rehabilitation and pulmonary rehabilitation) could save the U.S. healthcare system an estimated $7 billion per year.”

IQVIA

A congestive heart failure remote monitoring program initiated between Northern Arizona Healthcare and partners aimed to improve the management of patients with chronic diseases and/or high-risk conditions, by connecting patients with home-based medical devices and their care providers to ensure proper patient data collection and sharing of PGHD. An analysis of the program comparing data six months before and after implementation found an achievement of:

  • An average 44% reduction in readmission to the emergency room
  • An average 64% decrease in the number of days hospitalized
  • A reduction of $92,000 in per patient hospital charges

This program is a clear indication of the benefits to healthcare stakeholders that can be derived through successful healthcare data management.

The Dartmouth-Hitchcock Spine Center in Lebanon, NH, is a case study in the optimal use of PGHD to improve treatment outcomes. Patients complete a survey at home using a patient portal or in-office using a touchpad prior to their first visit at the center. The data are analyzed in real-time to create a summary report that is fed into the flow of care for use by the patient and the care provider. The provider also inserts some core clinical data elements into the clinical report and all information gets stored in a data warehouse for analysis and use in the care of the patient.

Overall results from a survey on the system found that over 80% of patients rated the system as“excellent to good” and one-third indicated that the system had led to positive changes in their visits. Approximately 50% of clinicians reported that the system saved time.

The Swedish Rheumatology Quality (SRQ) registry at the Karolinska University hospital, in Stockholm, Sweden, is another example where patient generated health data is incorporated into clinical care for optimal benefits.The SRQ registry is web-enabled and integrates real time, standardized data provided by patients, clinicians, and diagnostic tests. This data is used to improve the outcomes of care for individual patients, at the point of service as care is provided, and in the patient’s home to support self-management, as well as for quality improvement and research.

Efforts by patient care organizations to fit Digital Health tools into clinical practice has progressed, with 540 current clinical trials in the U.S. incorporating these tools and an estimated 20% of large health systems shifting from pilot Digital Health programs to more full-scale rollouts.
IQVIA

Researchers can benefit from well-executed healthcare data management. In one study of a framework for smartphone-enabled PGHD analysis, researchers at the Scripps Translational Science Institutelooked at blood pressure (BP) readings taken at variable times by persons in a study using a smartphone. They were able to detect an approximately 2 mmHg decrease in BP over a six-month trial, despite considerable intra- and inter-individual variation. This technique could prove useful for researchers in future study designs to analyze data as the field of digital medicine grows.

Of course, proper healthcare data management needs healthcare technology, and digital disease management solutions are available on the market to help with this. Ensure that selected solutions offer unified storage that is scalable and highly efficient to meet the requirements of multiple use cases.

Healthcare has become more patient-centric and policies such as value-based care will continue to push this movement forward. Patient generated health data has major potential for improving care, increasing patient engagement, lowering costs, and reducing wastes. However, there are many intricacies to properly leveraging PGHD to realize all these benefits. The process must include systems for proper healthcare data management which is inextricably linked to healthcare technology solutions.

Contact us at Acuma Health, where we assist healthcare stakeholders understand the digital health landscape including the benefits of PGHD, and provide you with solutions to solve your healthcare data management problems.





Data Collected from Seizure Monitors Can Improve Outcomes and Reduce Costs of Managing Chronic Conditions

Data Collected from Seizure Monitors Can Improve Outcomes and Reduce Costs of Managing Chronic Conditions

January 8, 2020

Patient Generated Health Data from Smart Watches Ensures Accurate Seizure Tracking and Reporting

Epilepsy is one of the most common neurological illnesses with estimates of approximately 3.4 million people in the US experiencing active epilepsy in 2015. Despite advancements in treatment options and optimal medication management, nearly one-third of patients with epilepsy continue to have seizures. Seizures can negatively impact the overall quality of a person’s life due to their unpredictable nature, occurring at anytime and anywhere. Healthcare technology  has come to the aid of people who have seizures with the development of seizure monitors or seizure alert devices that can detect the onset of a seizure and make an alert so that the individual suffering the seizure can be aided quickly. Good seizure monitors can also provide clinicians with detailed seizure data that can be used in the management of epilepsy.

There are many seizure alert devices on the market including seizure bed alarms or mattress sensors, seizure bracelets and smart watches, and camera/video/infrared devices. The report, “Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy,” explored various seizure detection and prediction systems and noted that accelerometers such as smart watches, detect changes in velocity and direction and may serve to detect motor seizures such as tonic–clonic or myoclonic seizures. The authors found that a smart watch was able to detect 7 out of 8 tonic–clonic seizures in a pilot study. It further noted that the SmartWatch, manufactured by Smart Monitor Inc.:

·         utilized pattern recognition and feature analysis in its built-in seizure detection algorithm

·         can synchronize with a smartphone application via Bluetooth to transmit seizure data to the user’s mobile phone

·         the app can then contact caretakers to alert them of ongoing seizures

It is evident that the patient generated health data collected by smart watches can provide accurate tracking and reporting of seizures. Seizure monitor technology and seizure alert devices are very useful for patient engagement and can produce improved patient outcomes through use of the patient generated health data that they provide. The use of seizure monitors such as smart watches allows for early intervention in patients experiencing seizures, preventing injury, lessening the severity of the seizure, and potentially preventing sudden unexpected death in epilepsy (SUDEP). Seizure monitors also provide objective data that can be leveraged by healthcare providers to adjust therapy, allowing for better management of the patient with epilepsy and resulting in cost savings from reduced hospitalizations.

To find out more about how smart watches and patient generated health data can ensure better management of patient care and costs, download the Guide to Leveraging Healthcare Technology to Improve Management of High Risk Patients.