6 Ways Providers Can Use Data to Lower Healthcare Costs
For nearly a quarter of a century now, growth in U.S. healthcare spending has accelerated beyond GDP growth, outpacing spending per capita in all other countries of comparable industrial development.
However, rising costs have not correlated with improved patient health outcomes as the U.S. now ranks 11th or higher in categories such as healthcare access, equity, and efficiency. Under these circumstances, healthcare organizations are increasingly asking how providers can lower healthcare costs while improving overall care quality.
Presently, the most promising answers come from big data and data management. This guide explores how healthcare providers can use the data they generate and store to reduce unnecessary expenses, streamline operations, and improve patient outcomes.
- Despite decades of rising healthcare costs in the U.S., the quality of care has not improved, suggesting that healthcare organizations have become less cost-effective.
- Implementing systems to better manage and analyze healthcare data will help providers improve care while reducing unnecessary expenses.
- Extracting value from healthcare data involves applying predictive analytics, data management platforms, and AI to data captured in different care provider systems.
Lowering Provider Healthcare Costs with Better Data Management and Analytics
Healthcare providers have a valuable, cost-reducing resource in their patient, clinical, and administrative data. By taking advantage of current technology in data management and data analytics, organizations can develop systems to extract this value.
1. Improve Data Quality
Healthcare organizations depend on data accessibility – for both provider directories and patient records – and interoperability in their day-to-day operations. Nevertheless, bad data in the form of incomplete, misleading, or incompatible entry fields is endemic to healthcare IT systems.
Studies show that healthcare organizations could save an average of $42.3 million over three years by implementing better data management controls and interoperability standards. Improvements to data quality have a positive, bottom-line impact on nearly every division of healthcare operations, from care costs to administration and IT.
2. Streamline Appointment Scheduling
When patients fail to schedule appointments, they suffer the effects of delayed care waiting to reschedule or simply forego treatment altogether. Deferred or skipped treatment drives up the cost of care – both for patients and providers – down the road. By maintaining current and accurate contact and service information, providers can drive down care costs across their associated networks.
3. Analyze Patient Histories to Identify Engagement Opportunities
Missed appointments and unfinished schedules of treatment and medication cause an avoidable substantial drain on provider time and resources.
Using patient history data, predictive analytics can identify patients who are likely to have no-show appointments or disregard physician instruction and prescription guidelines. Providers who integrate this data into CRMs can automate reminder contact and other forms of early intervention to monitor patient engagement and ensure that they deploy clinical resources effectively.
4. Intervene Earlier in Disease Progression
Early intervention in progressive diseases and conditions with a high incident rate for comorbidities not only saves lives and improves patient quality of life. It also prevents the snowballing costs of long-term intensive care. Until recently, accurately identifying progressive conditions early enough to prevent severe outcomes generally involved more luck than repeatable methodology.
Image Source: Developer.hpe
However, with advancements in the capacity for analytical tools, more insights from case studies that enable radically improved risk quantification have become available to providers. Already in areas such as cardiovascular disease and diabetes, providers who take advantage of available risk assessment data are delivering more effective preventative care and significantly cutting long-term care costs.
5. Collect Clinical Trial Data with AI
Collecting clinical trial data through traditionally manual methods is costly and time-consuming. Manual data transcription from hard copies and inputs scattered across unintegrated IT systems requires dedicated staff and – even in highly validated processes – introduces non-negligible errors. However, clinical trials are necessary for advancing treatment and understanding disease progression.
Owing to recent developments in AI technology, providers who participate in clinical trials now have a viable and cost-cutting alternative to traditional data collection methods. Today’s AI systems can handle a broad range of data management tasks, including unsupervised element tagging, downstream direction, report auto-population, and data visualization tools.
Image Source: Pharmafocusasia
Beyond replacing human labor for data entry and analysis, machine learning systems can discover complex data relationships across diverse data domains, increasing the range of insight for the same volume of data. Combined with the adoption of wearable devices that report automatically, AI dramatically drives down data collection costs while delivering more accurate output.
6. Reduce Hospital Overstay and Readmissions
Hospital overstays and readmissions constitute two of the costliest sources of resource leakage in healthcare organizations. Better management of critical, limited resources such as hospital beds and care provider time reduces overhead and improves the overall quality of patient care.
When organizations leave the monitoring inpatient care times to the discretion of individual care providers, tracking overstays against known averages becomes difficult. Overstays not only drain resources. They also increase patient exposure to secondary infections, compounding effects on patient health and care provider availability.
Alternatively, insufficient safeguards against early discharge result in patients needing to return for readmission. In both cases, applying predictive analytics to patient data and medical histories allows administrators to gauge the accuracy of discharge times for various conditions and implement more effective oversight and controls.
Real-Time Master Data Management for Healthcare Organizations with Gaine
Extracting maximum value from healthcare data requires the integration of dozens of contributing IT systems. When organizations rely on periodic IT tasks to generate consistent master data, users often access inaccurate master data in day-to-day activities. Coperor’s industry-first healthcare master data management platform integrates data across complex organizations and their networks of contracted partners in real time, enabling live master data visibility.
Contact Gaine today to learn more about Coperor’s capabilities for reducing healthcare costs.
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