Digital Health Data to Improve Results

Digital tools are rapidly increasing the amount of health information. It can and should help improve the quality of healthcare. This article outlines the case and what is required to make it happen.

These days, “digital health” is a popular topic. We should be able to understand more about our health and what we can do to fix it as data continues to accumulate through sources such as electronic health records, personal health apps and gadgets, home genome test kits, and personal fitness apps and gadgets.

However, having too much data is not enough. It is not enough to have much data. We need to be aware of and understand the meanings. Then, we can act accordingly.

How to Use Digital Health Data

How to Use Digital Health Data

The challenges in the United States are more severe because of the fragmented care system, but they also exist in other parts of health care.

Colon cancer can be curable and prevented if caught early enough to remove any precancerous growths. You can still be June today with recommended screening.

What happened? She was scheduled for colonoscopies at 50 and 60, but she thought she was good to go until 70. However, no one raised the issue with the radiologist’s note that a few minor irregularities required her to return at 63. June didn’t have to act on this finding.

It was not the job of the radiologist. The tab “Test Results” was in her electronic health record (EHR). It was not her fault. Her primary care physician missed it. The entire health care system missed it.

Too many Junes are wasted too quickly. These small mistakes can have huge consequences on the U.S. healthcare system. They cost Americans years of health and preventable treatment costs of billions of dollars.

Unsurprisingly, these failures happened when clinicians relied on paper folders, multipart forms, landlines phones, and paper folders to complete and track their tasks.

The internet, smartphones and computers allow patients to receive timely and consistent reminders, such as in June, to schedule their colonoscopies.

Digital tools can’t be used by themselves. We must tell them what they should do. The right combination of systems is needed to analyze and detect the data in June’s case.

They would then send it to her doctor and track their responses. Once she turns 63, it would be easy to “click here” and schedule her procedure. Follow up with any suspicious results and recommendations for treatment and tests.

Although the dangers of alert fatigue are real and should be avoided, staff and clinicians will appreciate properly designed reminders that can help to avoid missed or delayed diagnoses and the regrets that follow.

Perhaps the most challenging and important health care task of all time is to figure out how to create systems that can use an increasing amount and variety of digital data.

Our organization, the National Committee for Quality Assurance (NCQA), has been using data since the 1990s to improve and measure health care quality. Originally, it was used to accredit plans and later assess providers’ performance.

The challenge faced by the NCQA was to collect enough data and then draw inferences from the information that wasn’t there.

The challenge now is to find the essential information from the vast amount of data. NCQA’s mission is to put data to use to improve the effectiveness of resources dedicated to health care.

This article will describe the steps required to close the loop connecting digital information and action.

Measurement of Quality: The Basic Principles

Three questions are used to measure quality in health care:

  • Are we doing enough to ensure that our health is maintained?
  • Are we getting the results we desire?
  • What can we do to make it happen?

These questions rarely have simple answers. The outcome of any episode of care is not a function of one factor. Patients are not robots.

It is important to know if patients feel better about their care and how that compares with other care or treatment methods that may be more cost effective and provide a better outcome.

Although measuring the quality of care is hard, the United States’ current report card shows mixed results. The United States’ best care is often the best worldwide.

It is also known for spending the most on health care (19.7% GDP), twice as much as most peers, and for getting poor value for money. The U.S. maternal death rate is a disgrace.

It is more than twice that of Canada and four times as high as Sweden. Not to mention the growing and worsening disparities between races.

The gap between the average life expectancy of the United States and other peer countries is growing.

Health care stakeholders face a serious problem because of this incomplete and mixed picture of the quality of care.

Employers and health plans need to prove that they are getting value for their money. Providers must track their performance as payer contracts change from rewarding more services to rewarding higher outcomes.

Patients should be able to choose between providers and health plans based on quality if they have any.

To ensure that resources are allocated where they will have the greatest impact, regulators and lawmakers must understand the effectiveness and efficiency of medical services and providers.

There are many reasons why quality measurement in health care has not been developed. The first is that providers still receive a small portion of their revenue through quality-based reimbursement.

Consumers do not require them. Instead, they trust the advice of their doctor or family members who have been treated.

The main reason for the poor quality measurement is the reliance on insurance claims for its measurement.

Claims Data: An Incomplete Basis for Measuring Quality

Since the beginning of a data-driven effort by the health industry to measure quality in the 1990s, the industry has heavily relied on the analysis of insurance claims as the only consistent and large source for digital data across all providers.

Although claims data can give some insight, it is not always well-suited to other purposes. In this instance, the data was collected to pay the provider.

It’s often several months old when it is available for analysis. It’s also clinically incomplete. A claim does not show the result of something done.

A list of tasks completed in eye exams, blood sugar checks, and weight checks will show that a diabetic patient was treated but not whether her blood sugar levels were controlled.

If the provider is seeking more money, claims won’t include vital information about the patient’s full health. If the patient has comorbidity, they can bill for a diagnosis at a higher rate.

For example, a doctor could bill for treating a heart attack in a patient with diabetes. However, linking all of the claims may help reveal that the patient also has arthritis, reflux disease, and eczema.

Finally, each claim represents a partial view of one episode or service at a time. However, a pile of snapshots is not the same as a movie. Between the snapshots, there is a chance for improvement or worsening of health.

It’s too late to change the course of events by the time we take the photo. All we can do is look at the results and consider how we can improve next time.

Digital Measures: The Era of Digital Measures

We no longer need to rely on claims data. With the widespread adoption of electronic health records and federal incentive payments in 2010, the tide started to turn.

This monumental effort was overseen by the Office of the National Coordinator for Health Information Technology. It continues to promote and initiate ways to use EHR data.

Recent data streams have been added to that data from fitness trackers, smartphones, monitoring devices, and patients’ assessments of their health. Also, there are readily available population-level data about social factors that profoundly impact health.

These include employment status, income, environmental quality, community support, and other factors. Advanced analytics may combine all these data sources to provide a better picture of patient health and care effectiveness at all levels, from individual patients to whole communities to entire groups with the same diagnosis.

This is the demand side. The Centers for Medicare & Medicaid Services represents the demand side, which is the largest payer of U.S. healthcare.

They are actively encouraging the use of digital data for quality control. Because it is difficult to create “value-based contracts” without accurate measurements, commercial payers are also looking for better ways to measure value.

Our organization is currently developing digital measures to monitor the performance of the health insurance plans we accredit. These collectively cover more than half of the U.S. population. Every organization with a stake in quality health care is ready for a new era.

Learn from others

The United States can take lessons from developed countries using their digital data for improved health care. Denmark has a patient registry that dates back to the 1960s and a shared electronic health record system for all citizens.

The United States national digital health strategy is focused on the following: timely information, partnership with patients and prevention, equity, and equity.

Denmark can accomplish a much easier task than the United States due to its compact geography and 6 million inhabitants, but it also shows us what is possible.

The European Union is pursuing similar goals: It proposed the European Health Data Space to create a single digital market for its 450m people in May.

The United States efforts to improve digital measures are also valuable to other countries facing similar problems regarding access, quality, cost, and accessibility to health care.

To-Do List for Digital Measures

There are at least four imperatives to get the United States to where it needs to be:

Data collection can be reduced, and data processing times improved. Although this may seem like two goals, digital measures accomplish both. Traditional measures, such as those involving insurance claims, can lead to delayed care delivery by up to one year.

This can make them almost irrelevant in certain areas. Systems such as electronic health records or wearable devices can produce data as a result of managing care more efficiently and cheaper if designed correctly.

Data collection should no longer be a step apart from providing care. We can move straight to analysis and the results.

Increase the number of data that can be used. All sources mentioned earlier include EHRs and wearable health monitors; patients’ feedback about their health can be combined with data about the patient’s environment, such as water quality, crime rates and green space.

NCQA examines patients’ social situations, poverty, homelessness, isolation, access and availability to healthy food, exercise, and other activities. When evaluating the quality of their care. One doctor might recommend that a patient take a walk every day.

Great advice for someone close to a park, but not for someone who lives in high-crime areas and is afraid of leaving the house.

We will be able to create measures that better reflect the care needs of specific patients or groups.

We will be able to account for differences in care requirements depending on socio-economic factors, patients’ ability to manage their care and the quality of their social support.

Use the widespread adoption of electronic health records, mobile phones, and artificial intelligence to provide real-time feedback and guide care. Electronic health records go beyond simply keeping track of patients’ conditions and their care.

They provide real-time support, including alerts, reminders and computer-based guidelines for managing chronic diseases. This intelligent EHR would have reminded June, her doctor, to schedule the follow-up colonoscopy on her 63rd birthday.

Our systems of measuring quality care will become more sophisticated, and we will be able to integrate intelligence more tailored to patient’s needs and wants.

An intelligent EHR would see that June prefers Tuesdays for her appointments and would schedule the procedure for the next Tuesday with her consent.

Integrated health systems like Pennsylvania’s Geisinger and Intermountain Healthcare in Salt Lake City have created digital tools to improve patient care. However, both have the dual advantage of having advanced IT capabilities and the financial incentive as both providers or insurers to concentrate on improving patients’ health and not just delivering more services.

These and other organizations have used their electronic health records to give real-time feedback to patients and clinicians. These systems allow for more personalized patient feedback, leading to better care and better health decisions.

For the continuous production of quality measures, establish a digital foundation. Digital measures are not an end-all endeavour. It is a constant transformation. This foundation is created by the following:

The development of a standardization process standardizes the various measures currently in use.

The process must be thorough enough to allow for general agreement about, for instance, hypertensive blood pressure levels or test results showing diabetes well-controlled.

However, it should also be flexible enough to allow adjustments based on who is being measured. The current measures design differs for payers, regulators, professional societies, and other organizations.

This variation is more labour-intensive for providers to measure, but it almost certainly doesn’t deliver commensurate value.

The paper-based descriptions of quality measurements and the data required to replace them.

This process is costly and can lead to errors. It is possible to replace paper with software-based descriptions, which can easily be added to clinical systems.

Software tools allow for collaboration in the development, testing, maintenance, and improvement of measures. Each new illness or treatment will have its requirements.

This effort must include payers, regulators and providers as well as patient groups to help accelerate the development of new measures and reach a consensus about which ones to adopt.

Automating data extraction from electronic medical records is still common, rather than using data abstracters. This will lower the cost of collecting clinical data and improve its accuracy.

The Fast Healthcare Interoperability Resources standard (FHIR), a standard API that allows information to be exchanged between systems, already provides a solid platform for this. CMS will mandate that providers use FHIR-enabled systems starting next year.

They are automating auditing and cleaning of data. Many of the data in EHRs or other clinical systems are manually entered.

This can lead to errors, omissions, inconsistent entry practices, and other problems. Digital measures won’t have any value if they don’t contain the right data.

Every stakeholder in the health care system has a part to play, along with creating an infrastructure for digital information.

Quality measurement professionals need to increase and expand their efforts to identify the most relevant data elements for identifying best practices, explaining variation in outcomes, and determining which new data elements will be most useful.

Insurers and hospitals have legacy systems that cannot handle the demands of data exchange with other systems. These systems need to be upgraded, standardized, or find workarounds to meet the demands of digital measurement.

Hospitals and physicians are paid primarily based on care volume rather than quality. This reduces their motivation to redesign their care delivery process. Both payers and providers must adopt data-driven payment models based on value and effectiveness.

Employers and governments pay the majority of health care. They have a crucial role to play in using their power (e.g. contracts and the ability to move their provider or health plan business elsewhere) and to demand that providers, quality measurement communities, and health plans accelerate the adoption and development of digital quality measures.

Employers and governments can also use their expertise to assist the industry in understanding how they will use these measures to improve their health-care-benefit offerings.

Staff should be involved in forums establishing health data standards and the appropriate uses of data.

These insights must be readily available to patients to understand and evaluate them as they make their health and care decisions.

Digital Measures and Their Impact

What does it look like to harness this mass of data to manage and measure the quality of our healthcare?

Providers could more accurately and effectively evaluate and improve their performance. They could catch patients due for screenings, manage patients with chronic illnesses requiring hospitalization, or even prevent them from coming to the hospital.

Patients and their families could make better decisions. Patients could get the best care using digital tools that suggest where to eat or change their oil.

Employers and insurers could improve their health benefits coverage to meet the needs of employees and members better.

They could also pay for services that keep them healthy and find the best providers. They could also do this in real-time, or very close to it, rather than relying only on last year’s data.

Health care could be a data-driven powerhouse like retailing and financial services, except that it serves the purpose of saving lives and maintaining health.