To address quality and costs challenges, the concept of a value-based healthcare system is gaining significant ground. Articles on the subjects have recently appeared in prestigious publications such as the New England Journal of Medicine (NEJM) and Health Affairs. There is no clear consensus among industry experts that initiatives such as the Accountable Care Organization (ACO) and the Patient Centered Medical Home (PCMH) will lead to dramatic improvements in quality and costs reductions. However, everyone seems to agree that the ultimate goal of a healthcare system should be to maximize positive patient health outcomes per dollar spent.
Measuring Healthcare Value
Michael Porter is arguably the leading thinker on the subject of healthcare value. In an article titled "What Is Value in Health Care?" that appeared in the New England Journal of Medicine, he wrote:
"Value should always be defined around the customer, and in a well-functioning health care system, the creation of value for patients should determine the rewards for all other actors in the system."
Current process measures and the regulatory requirements to report them are necessary but not sufficient. It seems like the healthcare industry has escaped the business process reengineering and quality improvement movements that have permeated many industries in the nineties. Fortunately, thanks to the hard work of visionary leaders like Donald Berwick, former Administrator of the US Centers for Medicare & Medicaid Services (CMS) and co-founder of the Institute for Healthcare Improvement (IHI), quality measures reporting is now an essential component of incentive programs such as Meaningful Use and the ACO model. Healthcare transformation during the next few years will be focused on the migration from a paradigm based on the volume of services delivered to one that is based on measuring value (patient-centered outcomes) as well as the total costs incurred throughout the care cycle to achieve those outcomes.
Patient-centered outcome measures include essential metrics such as mortality, functional status, time to recovery, severity of side effects, and remission (e.g. depression remission at six and twelve months). These measures should take into account the values, goals, and wishes of the patient. Therefore patient-centered outcome should also include the patient's own evaluation of the care received. One important implication of this shift from fee-for-service to value is the growing importance of wellness, prevention, early screening, and disease and population management. In short, the elimination of waste and the optimization of healthcare delivery through the standardization of care pathways and treatment protocols based on the latest scientific evidence will become a top priority.
Measuring Healthcare Costs
The total costs incurred throughout the care cycle represent the assets used to achieve those outcomes such as: employees, consumables, facilities, medical devices, equipment, energy, and computing resources such as software and hardware. Measuring these costs would appear intuitive and straightforward at first. However, in the US healthcare system, Medicare reimbursement is based not on the actual usage of resources, but on so called relative value units (RVUs). Service charges are calculated based on formula that includes three RVUs: one for physician work, one for practice expense, and one for malpractice expense. Similar reimbursement schemes are used in other developed countries as well.
In an article titled "The Big Idea: How to Solve the Cost Crisis in Health Care", Michael Porter and Robert Kaplan proposed time-driven activity-based costing (TDABC) as a more accurate methodology for measuring costs in healthcare. Bundled Payment (using episodes of care as a basis for payment and value measurement) is emerging as a solution to contain healthcare costs.
Implications for Healthcare Information Technology
The trend toward bundle payments and other cost containment schemes will put an increasing pressure on providers to maximize the value of the assets involved in the care cycle. This will be achieved by optimizing the decision making process with software that can analyze large data sets and make specific and accurate recommendations faster than humanely possible.
In the clinical domain in particular, the following are examples of health IT solutions that will make a big difference in maximizing value for patients:
- Automated execution of Clinical Practice Guidelines (CPGs), Care Pathways (CPs), and treatment protocols using technologies such as business rules, predictive analytics, and Business Process Management (BPM).
- Creation of disease registries as well as secondary use of EHR data to track patient outcomes, compliance with CPGs, Comparative Effectiveness Research (CER), and Patient-Centered Outcome Research (PCOR). Advanced analytics will play an important role in providing important insights into clinical data on the effectiveness of various treatment options based on the clinical profile of a specific patient or subpopulation of patients. Personalized Medicine leveraging advances in genomics will play an important role here as well. This will require computing power to handle the large data sets involved in making clinical decisions based on genomic data.
- Applications of Knowledge Representation, Reasoning, Natural Language Processing (NLP), Speech Recognition, Information Retrieval, and Machine Learning to enable next generation Clinical Question Answering (CQA).
- Clinical Knowledge Management (CKM) to support a learning health system. The Institute of Medicine (IOM) released a report earlier this year titled "Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care". The report describes the learning health system as:
"delivery of best practice guidance at the point of choice, continuous learning and feedback in both health and health care, and seamless, ongoing communication among participants, all facilitated through the application of IT."
- Social Health Enterprise tools that allow clinicians to communicate and collaborate beyond email.
- Leveraging mobile devices and tablets to provide information and cognitive support to clinicians, patients, and care givers while enforcing strict security.
- Healthcare interoperability standards. As Doug Fridsma, Director of the Office of Standards and Interoperability puts it in a recent blog post: "standards are not optional". Standardization at the data, security, and transport level is necessary. However, care should be taken to ensure that these standards can be widely implemented by health IT vendors. Adopting healthcare profiles of cross-industry standards and creating Open Source reference implementations using the tools and techniques developers are familiar with (as was done by the ONC-sponsored Direct Project) can help meet that objective.
On the other hand, over-standardization in areas that are going through a rapid rate of technological innovation could have a negative impact.
- Applications of human factors research to enable the effective use of technology in clinical settings. Examples include: implementation of usability guidelines to reduce alert fatigue in clinical decision support (CDS) and the use of speech recognition, checklists, simulation, Visual Analytics, and disease-specific documentation templates or Smart Forms.
There are many lessons to be learned from other mission-critical industries that have adopted automation. Following several incidents and accidents related to the introduction of the "Glass Cockpit" about 25 years ago, human factors training known as Cockpit Resource Management (CRM) is now standard practice in the aviation industry.
- Lastly, Cloud Computing and Service-Oriented Architecture (SOA) will allow health enterprises to reduce costs and share computing resources while focusing on their core competency: medical care.