3 Patient Care Issues Data Management Can Solve
For most of the last decade, the large amount of highly trained, manual IT labor required to practice effective data management remained a barrier to entry for many organizations. However, recent innovations in data management automation technologies have brought the IT overhead down by nearly 20%. For healthcare organizations making the transition into data management, the incentives include both the potential for reduced operational costs in value-based care and better, data-enabled solutions to patient care issues.
Data management connects with the quality of patient care through the statistical and predictive analytics it enables organizations to extract from the data they capture. Organizations then apply these analytics to enhance decision-making processes. In this guide, you’ll learn what data management in healthcare is and how it can enable patient solutions.
- Data management in healthcare organizations can solve many patient care issues.
- Healthcare data management involves capturing, storing, and analyzing patient data and other data sources so that care providers can make more informed decisions at the point of care.
- Data management practices can help deliver comprehensive patient data to care providers in real time, drive patient engagement, and facilitate better resource management.
What is Data Management?
Data management refers to the set of organizational practices that treat data as a valuable resource. To derive value from data, organizations must have systems to capture, store, analyze, and protect the data their operations generate. In healthcare and the life sciences, the relevant data sources for data management are diverse, including:
- Patient health records
- State health records
- Data from medical devices
- Laboratory data
- Medical imaging services
- Data collected from search engines, smartphones, and wearable devices
Image Source: https://www.aha.org/aha-center-health-innovation-market-scan/2021-02-02-4-keys-becoming-data-driven-organization
In organizations of any kind, data management usually involves at least three separate spheres of activity.
1. Data Governance
Data governance primarily consists of assigning roles within an organization that establishes who’s responsible for creating and maintaining data management plans.
2. Data Quality Assurance
Depending on how they are captured and stored, large data sets may contain many errors and anomalies. Data quality assurance is the application of analytical tools to identify potentially bad or obsolete data and remove it.
3. Data Storage and Security
Storing and securing data requires adopting an on-premise storage architecture, in the cloud, or a hybrid of both. Factors to consider in each case include available budget, security obligations, and accessibility needs.
3 Patient Care Issues and Ways Data Management Can Solve Them
From private practices to hospital networks, healthcare providers can use data management to improve patient care. Here are three patient care issues data management can solve.
1. Injury or Death Caused by Preventable Medical Errors
In the U.S., roughly 400,000 hospitalized patients experience harm through preventable medical errors each year. With just the integration of EHRs into system-wide applications, organizations can prevent many of these errors by automatically flagging potentially contraindicated prescriptions and statistically anomalous treatment recommendations. Beyond preventing errors, having full digital access to patient records also helps care providers spot potential substance abuse patterns in patients’ treatment and prescription histories.
When all care providers – physicians, nurses, lab technicians, and others – have real-time access to managed, integrated patient data through digital tools like electronic health records (EHRs), they stand the best chance of making the most informed decisions possible at any given moment. Additionally, AI applications to patient data can prevent medical errors humans overlook.
2. Insufficient Communication Before and After Clinical Contact
Although the patient data your organization collects and manages has broad applications in patient care, its effectiveness depends entirely on having patients currently under direct care. Nevertheless, patients rarely understand the earliest symptoms of disease lifecycles and many fail to adhere to treatment plans – either through negligence or misunderstanding of physician instructions – once discharged.
The timeframes before and after patients decide to enter care are equally – if not more – important for long-term health outcomes. Just in the field of oncology, for example, early detection of bowel, ovarian, and breast cancers – by screenings before the onset of symptoms – gives nine out of ten patients an expected survival term of five years or longer.
On the other side of clinical care, patients often suffer unnecessary resurgences of symptoms and declines in health following discharge. Among recently discharged patients, 20% experience worsening conditions within three weeks of discharge, 75% of which are preventable with sufficient patient-doctor communication and appropriate care.
The key to extending more continuous care both before and after hospitalizations and clinical visits depends on healthcare organizations leveraging the same kind of omnichannel marketing and communication that increasingly defines the retail world. Driven by managed patient data, patient marketing, and communication across multiple digital channels can reach patients earlier and drive early detection.
With younger patients already showing a preference for retail clinics and virtual interactions over traditional office visits, organizations can expect high engagement in these channels. Similarly, prompt post-discharge digital engagement reduces readmissions in patients with multiple chronic diseases by 20%.
3. Unnecessary Hospital Overstays and Readmissions
Patient data management can yield statistical and analytical insights throughout your organization. Even data that does not directly concern diagnoses or treatment can have indirect applications that ultimately affect patient health.
Effectively managing limited resources such as hospital rooms and beds enables organizations to treat more patients. Using patient data to measure average hospital stay durations for various conditions can help care providers identify potential overstays. Cutting down on unnecessary overstays not only drives down costs and frees up resources. It also limits unnecessary patient exposure to secondary infections.
At the same time, data on patient hospital stays can help administrators catch abnormally early discharges that may result in avoidable readmissions. In both cases, the result is improved patient care combined with cost reduction and better resource allocation.
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