| | August 20188UTLOOK Healthcare Tech IN MY OPINIONThe Promise of Big Data for Measuring Outcomes and Performance in Health CareBy Hon Pak, Chief Medical Officer, 3M HISHospitals are facing tremendous pressure to remain profitable. A recent study from the Congressional Budget Office suggests that absent productivity growth, 51--60 percent of hospitals could have negative margins by 2025. With the rising U.S. healthcare costs, $765 billion a year is estimated in waste, and there's uncertainty about how much risk healthcare systems can and should take on. The solutions are clear: Organizations must drive revenue through consolidation, improve coding and clinical documentation to promote accurate reimbursement, and lower costs by reducing preventable events and avoidable care--while learning about the value-based care world.The healthcare industry is applying data analytics to address the need for revenue generation, gain insight into waste and care variation, and measure the impact of various quality i mpr o v e me n t initiatives. Given current cost pressures, organizations need data and actionable information to effectively measure total cost of care and a l l o c at e scarce resources to areas that have the highest probability of meeting outcome and financial goals.Big Data in Value-based Care:Applying artificial intelligence (AI) and other tools to a longitudinal set of patient records and accessing a comprehensive view of the patient based on clinical, claims, patient-generated, and socio-economic data, can accelerate our journey toward value-based care and precision medicine. Unfortunately, most organizations have limited data, expertise, and resources to harness Big Data. Organizations are now hiring data scientists to manage the complexity of data quality, unstructured data, and lack of a single standard or tools (like risk stratification and attribution). Many have heard of the "4Vs of Big Data" (velocity, volume, variety, and veracity), but most overlook the importance of the fourth `V' which represents the most difficult aspect given the heterogeneity, diversity, and complexity of healthcare language and data.Too Many MeasuresThe proliferation of quality measures endorsed by organizations such as the National Quality Forum has made profiling and identifying potential quality problems increasingly complex. For payment applications, diverse quality measures must be combined and incorporated into systems to determine the net impact on payment. For most quality measures, there is no direct link to the cost or payment associated with the quality aspect being measured. Many of the quality measures are process measures, such as adherence to sterile techniques in the OR. The challenge of physician burnout increases with the ever-expanding documentation requirements for these measures.The significant issues with data quality and documenting care variation within EHR systems are due to varying physician preferences and documentation styles, lack of standardized Hon Pak
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