- Measuring Health Care: Using Quality Data for Operational, Financial, and Clinical Improvement
- Calculating the relative risk score
- There’s a right kind of data for improving healthcare
New Marketplace. Key Components of Performing Risk Management in Healthcare To navigate the healthcare risk continuum healthcare organizations and risk managers need to: Identify Risk Since risk management involves managing uncertainty and new risk is constantly emerging, it is challenging to recognize all the threats a healthcare entity faces.
However, through the use of data , institutional and industry knowledge, and by engaging everyone — patients, employees, administrators, and payers—healthcare risk managers can uncover threats and potentially compensatory events that otherwise would be hard to anticipate. To accomplish this, risk matrices and heat maps can be deployed that will also help to visualize risks and promote communication and collaborative decision-making. Having an established plan in place promotes calm and measured response and transparency by staff and ensures that corrective actions can be implemented and evaluated.
Sentinel events are not always the result of errors. However, achieving transparency and thorough evaluation requires healthcare organizations to establish an atmosphere of respect, trust, and cooperation between staff and leadership. Perform Compliance Reporting As with the Joint Commission, Federal, state, and other oversight bodies mandate reporting of certain types of incidents including sentinel events, medication errors, and medical device malfunctions.
Incidents such as wrong-site or patient surgery, workplace injuries, medication errors, etc. These are often the best way to identify and prevent risk.
Measuring Health Care: Using Quality Data for Operational, Financial, and Clinical Improvement
Healthcare providers should develop a culture that encourages reporting so that prevention measures and best practices can be instituted. Think Beyond the Obvious to Uncover Latent Failures Active failures are obvious and easily-identified — when a nurse gives the wrong medication dose to a patient for example. Latent failures, on the other hand, are often hidden and only uncovered through analysis and critical examination.
Was the nurse rushing because he had too many high-acuity patients? When exploring the causes of an unfavorable episode, consider underlying and less-readily-apparent reasons. Deploy Proven Analysis Models for Incident Investigation Models for analyzing accidents are used to understand latent failures and causes as well as relationships among risks.
For example, understaffing and fatigue often lead to medical errors. Applying well-established models improves risk management effectiveness and efficiency. Two accident analysis models used in healthcare risk management are the and the Sharp and Blunt End Evaluation of Clinical Errors model. FMEA or Failure Mode and Effects Analysis, as well as Root Cause Analysis , are also deployed and involve detailed frameworks to help uncover the causes and effects of medical mistakes.
These systems provide tools for documenting incidents, tracking risk, reporting trends, benchmarking data points, and making industry comparisons. Reports can be generated for losses, incidents, open claims, and lost work time for injured employees to name a few. RMIS can greatly enhance risk management by improving performance through available and reliable systems while providing overall cost reduction by automating routine tasks.
It includes risk transfer usually through insurance policies and risk retention such as self-insurance and captive insurance. Create a Healthcare Risk Management Plan Healthcare organizations need to have an established and on-going risk management plan in place. Response times, staff responsibilities, and prescribed actions need to be articulated and communicated.
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Additionally, reporting on quantifiable and actionable data should be detailed and mandated by the plan. Communication Plan While it is critical that the healthcare risk management team promote open and spontaneous dialogue, information about how to communicate about risk and with whom should be provided in the healthcare risk management plan.
Next steps and follow-up activities should be documented.
Calculating the relative risk score
In many cases, the measures have been developed specifically to support a local quality improvement initiative, rather than the other way around. I was able to use the data from Partner for Quality Care and compare it to my own patient registry. Our office has already instituted a change in our phone advice protocol that affect how asthma medication refills are handled.
Download Report More information on the roles Regional Health Improvement Collaboratives are playing in helping healthcare providers improve their performance is available here. Because of these partnerships, we will be able to spread and sustain the improvements that lead to better patient care.
Meet our Members Mylia Christensen. The Iowa Healthcare Collaborative issues a detailed report with extensive measures of the quality and safety of patient care in hospitals in Iowa. The Greater Detroit Area Health Council issues reports on a wide range of measures of the quality and safety of patient care in hospitals in southeastern Michigan.
The Washington Health Alliance issues an extensive analysis of health plan quality and services, rating health plans on over three dozen different items.
There’s a right kind of data for improving healthcare
It included more than , patient-completed surveys on patient experience of care from clinics around Minnesota. The next round of results will be released in Minnesota Community Measurement reports on the costs at different healthcare providers for procedures ranging from colonoscopies to labor and delivery. Quality Quest for Health reports on the rate at which physicians prescribe generic drugs for their patients.
Generic prescribing rates are reported for primary care physicians and a number of specialties.
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The Washington Health Alliance compiles quality measures separately for patients with commercial insurance and patients whose healthcare is paid for by the state Medicaid program, and the Alliance highlights areas where there are significant differences. Macias, Travis L. Rodkey, Krystle A. Bartley, and Heidi V. Russell declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent. This article does not contain any studies with human or animal subjects performed by any of the authors. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Rodkey Heidi V.
Part of the following topical collections: Topical Collection on Quality Improvement. Introduction Fragmented medical care, variation in care delivery, and exorbitant costs are all signs of a health care system that needs to change. Big data Although those in the health care industry are very familiar with the use of data i.
Enterprise data warehouse EDW Limitations of data management in health care are illustrated by the silos of data collection and storage in the typical IT structure. Clinical system integration CSI Converting this plethora of data into meaningful interpretations through analytics is only the first step toward achieving the ultimate conversion of digital storage into knowledge that will drive improved outcomes. CSI provides a conceptual framework for a nimble yet deliberate process of translating data for synchronizing care delivery across silos of care.
Simply stated, CSI facilitates the coordination of care across conditions, providers, settings, and time to achieve high-quality care [ 11 ]. Table 1 Key principles for successful health systems integration. Domain Definition Example s Comprehensive services across the continuum Plan for and coordinate core services along the continuum of care for the populations served e.
Workflows are facilitated through decision support tools including within the EMR Performance management Systems designed to monitor process and outcomes to identify opportunities for further improvement. These systems can be associated with compensation Balanced scorecard reports that link clinical, operation, and financial measurements for a disease continuum, health care service, or individual provider Information systems System wide computerized information systems for data management, tracking of utilization and outcomes, and enhancement of communication Using the EMR and EDW for population health analytics to drive quality improvement efforts with real-time or near-time dashboards and decision support tools Organizational culture and leadership Consistent leadership and engagement by providers and administrators with a vision for transforming care for better outcomes Governance structures that support clinical, operational, and financial initiatives across an organization Physician integration Including physician input and leadership into all levels of the system Educating physicians in quality improvement science.
Recruiting physicians to participate in the development, implementation and evaluation of evidence based guidelines, summaries, pathways and protocols Governance structure Bringing together organizations and services into a coordinated, mutually supportive, integrated system Representation by services across a health care service to develop and oversee the application of data from the EMR and EDW towards improving outcomes Financial management Integration for better cost control and strategic investment in outcomes that gain clinical value Integration of finance personnel at all levels of CSI governance and within workgroups makes cost-effectiveness of improvements integrated and transparent.
A robust data strategy was necessary for the clinical systems integration model to effectively transform large volumes of high-velocity data into meaningful knowledge. Thus, three domains were linked to this quality improvement effort, for which this article will focus on the domain of data management and analytics: 1. Once the target conditions were selected, care process teams were charged with the task of defining the patient population while parallel teams developed balanced scorecards of metrics that spoke to the highest quality of care for the area of focus.
In parallel processes, a modified Delphi approach was employed to select quality measures for that disease process based on their importance, scientific acceptability, usability, and feasibility [ 19 ]. The care process teams then developed specific aims for rapid cycle process improvements utilizing internal data derived from the EDW in near-time fashion to drive each subsequent cycle.
Asthma was our first CSI initiative. The care process team began their improvement efforts by building clinical decision support into the EMR through an evidence-based order set. This guided providers toward a pre-determined course of action and decreased variation.