Industry leaders and legislators alike are still debating whether the Affordable Care Act promotes or hinders innovation, but it’s inarguable that healthcare technologies will continue to move forward, regardless of speed. But, even with regulatory issues aside, there’s often a disconnect between media hype and the actual timeline for mainstream adoption of particular forms of innovation. For a simple example, consider Siri and Alexa, two competing products that have already become household names, and yet both represent how far virtual assistants still have to go in terms of AI capabilities. Emerging products and services, especially in the healthcare sector, can often get overhyped before early adopters even begin to take hold. Only time can tell whether that excess hype stemmed from making unrealistic promises, or the technology made an unpredicted shift in course.

That’s why Gartner, an international research and advisory company, created its Hype Cycle model (http://tekmountain.com/believe-the-hype/), a standard adoption model that plots out the typical trajectory of innovation, regardless of its industry, from when it’s first introduced all the way to when it becomes everyday technology. For Part Three of our Hype Cycle series, we’ll examine Gartner’s 2016 Hype Cycle for Healthcare Providers by giving an overview of a promising technology from each of the five phases of the Hype Cycle.

 

For clarification of each Hype Cycle phase, check out our Gartner intro blog 

 

2016 Hype Cycle for Healthcare Providers

Innovation Trigger

Value-Based Performance Management Analytics (5-10 years)

Gartner classifies this technology as a complement to population health analytics, though the former operates within a broader scope, as population health is a subset of value-based care. As there is a variety of existing healthcare payment models, this sort of analytics will help to ensure provider networks, while transitioning toward a strictly value-based model, are still able to “model, forecast, and monitor the performance of risk-bearing and value-based contracts, and to intersect the critical cost and quality variables,” regardless of which payment models are used across their populations.

Peak of Inflated Expectations

Protected Health Information Consent Management (5-10 years)

One of the major roadblocks to healthcare’s interoperability problem is navigating the various state and federal privacy laws regarding patient consent in sharing personal medical/health data. Consent-management technology hopes to solve that issue via “a system, process, or set of policies for allowing consumers and patients to determine what health information they are willing to permit their various care providers to access.” Health Information Exchanges, especially, can be helped by consent management, whether a patient is traveling throughout a single network or between competing networks. Via current practice, patients essentially choose between all or none of their medical/health data being shared, but consent management technology offers much more of an à la carte approach.

Trough of Disillusionment

Computer-Assisted Clinical Documentation Improvement (5-10 years)

Clinical documentation is one of the major components of healthy patients and healthy revenue cycles. Not only is it a point-of-care record of the patient’s health status, it’s also a means for optimizing population health management through “quality reporting, physician report cards, reimbursement, public health data, and disease tracking and trending.” As deep learning and other machine learning methods are integrated into clinical documentation assistant software, this technology allows for less clerical mistakes, but more importantly it also offers diagnostic advice while integrating the particular patient’s medical/health data into broader population analytics.

Slope of Enlightenment

Integrated Clinical/Business Enterprise Data Warehouse (> 2 years)

The terms clinical data repository and clinical data warehouse are often used interchangeably, but there’s a major difference between the two: the former is merely a database of raw data that’s designed for multiple uses, while the latter is specifically organized for analytics. Warehouses mostly have been used for clinical research or financial and and organizational analysis, but current efforts are aimed at utilizing them for quality improvement measures—specifically harnessing data to help improve clinical and administrative performance in a manner that boosts the quality of patient outcomes.

Plateau of Productivity

Generation 3 Enterprise Patient Financial Systems (2-5 years)

Enterprise-level patient financial systems allow larger provider networks to give patients personalized financial solutions throughout all of the provider’s healthcare systems. This technology typically centers around:

  • financial management workflow automation
  • financial management workflow analytics
  • patient affordability tools
  • easy and comprehensive integration with EHRs

Companies like Loyale and Patientco are looking to help providers not only cut down on workflow inefficiency costs, but also provide the patients with the widest access to affordable payment plans, which would help to reduce the heavy costs of unpaid medical bills.

Conclusion

As a nationally-recognized entrepreneurial and innovation center, tekMountain strives to provide game-changing consultation on implementing new technologies within a variety of industries, With healthcare as one of our three core focuses, we look to help guide any healthcare institution in deciding which of the latest products and services they can most benefit from. Backed by Fortune 500 company and compliance solutions provider CastleBranch, tekMountain not only can ensure you’ve got your hands on the right innovation, but that when you integrate that innovation it satisfies all federal and state regulations.

Contact tekMountain to learn more about how your healthcare company can realize the future today.

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