Feature

Healthcare Needs Intelligent Analysis

When organisations are able to slide and dice and have analytical capabilities, in turn providing intelligence from the data, they are able to uncover very important patterns and best practices

"To realise the real value potential of EMR, the industry needs a more holistic approach - the ability to combine the EMR data with other types of data and analyse it to know about trends and opportunities" Ashit Panjwani

Modern day healthcare is a technological marvel. Microscopic laser pulses reshape the human cornea to restore perfect vision. Magnetic resonance imagery and computerised axial tomography show us intimate details of human tissue or even the human brain. Robotic systems with tiny cameras and instruments perform delicate microsurgeries. Sound-waves create 3D images of unborn babies. Artificial hearts made of titanium and plastic sustain the lives of humans after their natural hearts have failed. Yet, it is annoying if you can't find the chart for a new patient being transferred from a clinic or decipher the scrawled prescription or get a complete history of a patient who you can't remember.

Today, the top priority of all healthcare executives is to assure the delivery of high-quality, cost-effective healthcare service. Apart from the desire to do the right thing for patients and families, there are several drivers within the healthcare industry that have pushed the quality agenda to greater heights. There has been a heightened awareness at all levels of the issues surrounding healthcare quality and patient risk. Healthcare leaders are challenged to improve care delivery, reduce costs and enhance physician and employee engagement. At a time when computers enable businesses to manage and locate everything from dry-cleaning to used trucks, few healthcare providers are able to access and track the medical records of patients across the continuum of care. Most large acute care providers are aggressively tackling the electronic medical record deficiency, but providers in thousands of solo and small office practices, where the majority of care is delivered, show little progress towards that goal.

EMRs Not Sufficient

In today's times, Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) hold great promise to improve the efficiency and quality of patient care while reducing costs and errors. The development of standard data models and a standard lexicon for coding patient care is accelerating the transition to paperless practice. However, EMR and EHR systems alone may not offer all of the benefits that they should. These systems will automate transactions, support research and make records more accessible, but will they answer elemental questions about the quality of those patient interactions like:

• What treatment regiment yields the best outcomes for patients with this genetic profile?
• What insidious drug interactions are we likely to see in a patient with these risk factors?
• How well does this combination of therapies help patients undergoing this procedure?
• What protocol produces the best rehabilitation results for this target population?

To answer questions such as these clinicians and researchers need business intelligence and analytics.

Business Goes Intelligent

Business intelligence and analytics is being used today by healthcare providers and life-science industries to run their businesses better. In fact with new sources of digital data about patients and their clinical experiences, it is being applied to not just improve medical practices but the practice of medicine itself.

The near-term evolution is already exciting. Traditionally, medicine focused on diagnosing and treating existing conditions, with limited data links across the continuum of care. In the emerging stage of data-powered healthcare, predictive analytics are being applied to reduce errors and to improve outcomes —incorporating new data sources such as genomic data and digital diagnostic images. The logical extension of this evolution is personalised medicine —holistic, pre-symptomatic treatment focused on preventing conditions rather than treating them after the fact.

The common denominator for these evolutionary stages is data —the ability to gather, cleanse and analyse extremely diverse data. It is no wonder that the healthcare industry has a long way to go to achieve evidence-based care delivery. Even with the best of intentions, healthcare leaders are often hampered in their attempts to become data-driven by the current state of healthcare data. The many disparate data sources usually do not speak to each other, and each may be owned and managed by different groups that report to different organisational entities. In many cases, just getting to the data is extremely labour-intensive, with no assurance that the data will be accurate or timely once you get to it.

Frontline managers and executives who are trying to solve problems from a variety of perspectives (clinical, financial, employee, patient) often are unable to fit these puzzle pieces together in order to understand what is driving performance— let alone how to improve it. When resources such as analysts or management engineers are in place, too often their time is spent just getting to the data and generating paper or electronic reports. They have little time left to truly analyse results and to understand the correlations and potential cause-and-effect relationships between the metrics.

Data Management

Effective data management is the foundation for communicating strategy and for achieving strategic alignment throughout the organisation. This requires putting the pieces together to encompass clinical and operational, financial, human resource, satisfaction, planning, governance and supplier data.

The adoption of EMR systems will no doubt be a boon to efficiency and effectiveness. Healthcare organisations can save time, reduce costs and improve processes by automating patient care transactions, such as appointment scheduling, medication orders, lab tests and billing. However, transactional EMR systems will go only so far in delivering ROI. Alone these would not provide the insights that would enhance the quality of patient care and the practice of medicine in general.

To realise the real value potential of EMR/ HER, the industry needs a more holistic approach — the ability to combine the EMR data with other types of data (lab, financial, operational, research etc.) and analyse it to reveal the hidden knowledge it contains about trends and opportunities. This calls for business intelligence and analytics.

Today, healthcare companies are building data repositories with information culled from operational systems used to record patient admissions and discharges, bill patients and insurers, order laboratory and radiological tests and dispense medications. The data can be analysed to judge alternative treatments such as 'heart bypass surgery V/s angioplasty' by looking at how quickly patients are discharged and whether they are readmitted. They can also study which medicines work best, spot previously un-recognised disease patterns, identify at-risk patients and even review the performance of individual patients.

When organisations are able to slide and dice and have analytical capabilities, in turn providing intelligence from the data, they are able to uncover very important patterns and best practices.

Evidence-based medicine focuses on how medicine is practiced. Protocol driven medicine describes a methodology for advancing research through clinical practice. Personalised medicine describes an ideal but achievable future state of customised care. With EMR/ EHR coupled with analytics, the healthcare industry is advancing forward in to all three ideals.

The writer is Executive Director Sales, Marketing, Channel & Alliances SAS India

Ashit.Panjwani@sas.com