Achieving a return with analytics: Data liquidity
Integrated information is essential to success with accountable care, says Will Bryant
Emerging information technologies enable providers to collect and analyze data from virtually all ends of the care system, improve clinical outcomes and engage patients as never before—key drivers of accountable care organizations and population-based healthcare delivery.
Data liquidity is the ability of data to flow throughout the healthcare system easily and securely. Information is quickly available to clinicians, patients and others, whenever and wherever they need it. However, the reality is that many organizations adopting these new technologies experience an overwhelming flood of data from multiple sources that they are ill-prepared to manage, let alone benefit from.
How do you ensure the best possible clinical and operational decisions throughout your complex organization? Rethink your business and analytics approach and make information visible—and meaningful—where it’s needed.
In a article published earlier, the author outlined how to create an effective data management program that ties information collection and reporting to business and clinical objectives. The second step is to construct a multi-directional pathway that distributes information to decision makers at all levels of the organization—your data integration strategy.
A truly integrated data system shows the following characteristics.
Your data management program is reliable and flexible. Harmonize, standardize and make data interoperable across systems within your organization and with partners such as other providers, pharmacies, health information exchanges and health plans. Update your program as circumstances change. This could be in response to mergers and acquisitions or when partners implement electronic health records systems, for example, and as the industry moves towards combining claims and clinical data. Data portability is a growing priority and will assist you in meeting clinical and financial goals as patients and systems continue to shift.
Patient data is complete and comprehensible to those that use it. Your system must be able to interpret and include data from all sources that touch your patients inside and outside your organization. Population health management in particular requires accurate, longitudinal information that supports predictive analysis. This includes clinical and financial data integration so you can identify focus populations, proactively undertake the best clinical course for each, measure and improve outcomes, and realize financial gains.
Data is consistently gathered and provides meaningful context. Metadata, information that is not overtly visible to users, makes information relevant and fluid. It describes the source of data, its history and definitions and tells the back end and front end how to interpret the data, functioning as a decision tree. For example, the medical record might identify a patient as a nonsmoker. If the metadata knows the patient previously smoked for 10 years and that the question was last asked 18 months ago, the system can prompt the clinician to confirm the patient’s current status. Set up correctly, metadata distinguishes data points on the back end that improve the collection process and increase data certainty. Patients don’t have to repeatedly answer the same question, and clinicians have more nuanced information upon which to make global as well as individual patient decisions.
Patients engage in clinical self-reporting and status review. Capturing data generated by patients through Internet-based logs, portals and mobile tracking applications gives clinicians more complete and timely input to care decisions. Access to an integrated medical record gives patients similar advantages—the ability to see and follow test results, and refresh themselves about recommendations as needed so they can make better personal health decisions.
Contrary to conventional wisdom, organizations should capitalize on the data they have in hand while building and implementing their longer-term data integration initiative. Inconsistent and incomplete data is still better than no data; as your program advances data quality will increase. In the meantime, you can discover gaps and areas for clinical process improvement. Dive into your data and learn as you progress toward a fully integrated program.
Emerging care and payment models demand highly accurate, timely information that flows to and from every corner of the organization. Technology, care delivery and patient engagement trends are progressing rapidly, adding to the sense of urgency for data management and analytics programs that instill confidence in measured outcomes and decision making.
Create a solid foundation that specifies what and how you will collect complex information and then develop an integrated system that makes data clear and understandable to a wide array of users. Despite external pressures to “get it all done yesterday,” it is in your best interest to be inclusive and plan carefully as you build your program.
Decisions by individual providers as well as programs need to be informed ones. Data liquidity will increasingly drive your organization’s health, as well as the health of its patients.
Data liquidity is the ability of data to flow throughout the healthcare system easily and securely. Information is quickly available to clinicians, patients and others, whenever and wherever they need it. However, the reality is that many organizations adopting these new technologies experience an overwhelming flood of data from multiple sources that they are ill-prepared to manage, let alone benefit from.
How do you ensure the best possible clinical and operational decisions throughout your complex organization? Rethink your business and analytics approach and make information visible—and meaningful—where it’s needed.
In a article published earlier, the author outlined how to create an effective data management program that ties information collection and reporting to business and clinical objectives. The second step is to construct a multi-directional pathway that distributes information to decision makers at all levels of the organization—your data integration strategy.
A truly integrated data system shows the following characteristics.
Your data management program is reliable and flexible. Harmonize, standardize and make data interoperable across systems within your organization and with partners such as other providers, pharmacies, health information exchanges and health plans. Update your program as circumstances change. This could be in response to mergers and acquisitions or when partners implement electronic health records systems, for example, and as the industry moves towards combining claims and clinical data. Data portability is a growing priority and will assist you in meeting clinical and financial goals as patients and systems continue to shift.
Patient data is complete and comprehensible to those that use it. Your system must be able to interpret and include data from all sources that touch your patients inside and outside your organization. Population health management in particular requires accurate, longitudinal information that supports predictive analysis. This includes clinical and financial data integration so you can identify focus populations, proactively undertake the best clinical course for each, measure and improve outcomes, and realize financial gains.
Data is consistently gathered and provides meaningful context. Metadata, information that is not overtly visible to users, makes information relevant and fluid. It describes the source of data, its history and definitions and tells the back end and front end how to interpret the data, functioning as a decision tree. For example, the medical record might identify a patient as a nonsmoker. If the metadata knows the patient previously smoked for 10 years and that the question was last asked 18 months ago, the system can prompt the clinician to confirm the patient’s current status. Set up correctly, metadata distinguishes data points on the back end that improve the collection process and increase data certainty. Patients don’t have to repeatedly answer the same question, and clinicians have more nuanced information upon which to make global as well as individual patient decisions.
Patients engage in clinical self-reporting and status review. Capturing data generated by patients through Internet-based logs, portals and mobile tracking applications gives clinicians more complete and timely input to care decisions. Access to an integrated medical record gives patients similar advantages—the ability to see and follow test results, and refresh themselves about recommendations as needed so they can make better personal health decisions.
Contrary to conventional wisdom, organizations should capitalize on the data they have in hand while building and implementing their longer-term data integration initiative. Inconsistent and incomplete data is still better than no data; as your program advances data quality will increase. In the meantime, you can discover gaps and areas for clinical process improvement. Dive into your data and learn as you progress toward a fully integrated program.
Emerging care and payment models demand highly accurate, timely information that flows to and from every corner of the organization. Technology, care delivery and patient engagement trends are progressing rapidly, adding to the sense of urgency for data management and analytics programs that instill confidence in measured outcomes and decision making.
Create a solid foundation that specifies what and how you will collect complex information and then develop an integrated system that makes data clear and understandable to a wide array of users. Despite external pressures to “get it all done yesterday,” it is in your best interest to be inclusive and plan carefully as you build your program.
Decisions by individual providers as well as programs need to be informed ones. Data liquidity will increasingly drive your organization’s health, as well as the health of its patients.
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