Mental health treatment is always changing, and using data can drastically improve your patient outcomes. While electronic medical records (EMRs) are primarily used for documenting patient information, they have incredible potential as a tool for transforming how treatment is delivered on a larger scale. Using data-driven insights, mental health professionals can optimize care, predict risks, and tailor treatment plans to individual patients, leading to better outcomes, which is especially important in the value-based care model.
Could EMR data hold the key to unlocking better treatment?
EMR systems gather vast amounts of patient data. On an individual level, this is a window into a patient’s history and how they might move forward. When considered on a larger scale, say, across an entire treatment network, administrators and medical directors can make community- and outcome-informed decisions that influence their treatment programming.
Behavioral health professionals can use various data points that EMRs track, including:
- Psychiatric history and diagnoses
- Medication prescriptions and responses
- Therapy session notes
- Progress assessments and outcome measures
- Co-occurring conditions
When these data points can be securely and unidentifiably tracked across an entire census population, they can be filtered for different factors that lend important insight into treatment planning. Individual treatment plans no longer rely on disparate knowledge and trust in the treatment process. They can start with a better understanding of how patients with similar circumstances make progress with different interventions.
Tracking therapy progress with actionable data
Effective mental health care requires an understanding of the progress a patient makes during treatment. EMR systems for behavioral healthcare simplify progress tracking by recording detailed therapy notes, symptom scales, and patient-reported outcomes.
For example, if a patient is undergoing cognitive-behavioral therapy (CBT), a clinician can track the changes in depression or anxiety scores over weeks or months. EMR systems enable mental health providers to visualize these trends, ensuring that they can make timely adjustments to treatment plans if a patient isn’t progressing as expected.
Take it further
Use reporting features within EMRs to compare therapy progress across multiple sessions. This can be done with a single patient, allowing you to look at their individual progress. You may even be able to set an automatic notification based on symptom scores to let you know if something seems off and you need to take a different approach.
Predicting relapse rates
Relapse is a critical concern, yet it’s often predictable with the right data. EMRs consolidate historical data, which can help mental health professionals identify factors that signal relapse risk. Examining trends such as missed appointments, medication non-compliance, or increases in specific symptoms provides early warning signs.
For instance, data might reveal that a patient with bipolar disorder experiences a relapse following disrupted sleep patterns or during seasonal changes. By identifying these triggers using EMR data, providers can establish proactive measures, such as scheduling extra check-ins or adjusting medication dosages.
Take it further
Say you notice a trend that patients with chronic depression are more likely to relapse after skipping two or more therapy sessions. With this insight, you can schedule automated reminders or outreach efforts to maintain treatment adherence.
Bringing data together for personalized treatment
One-size-fits-all treatment plans don’t work for mental health care, and EMR data is invaluable when creating personalized treatment plans. The ability to integrate structured data (like symptom severity scores) with unstructured data (such as therapist notes) enables practitioners to provide care tailored to individual patient needs.
For example, if EMR data reveals that a patient responds better to therapy sessions conducted in the morning due to improved cognitive function, session scheduling can be adjusted accordingly. Similarly, analyzing medication efficacy across a patient population can help practitioners refine prescription guidelines.
Take it further
AI assistance can help create session notes with the review of the therapist who delivered the session. Then, it can leverage built-in analytics tools in your EMR system to create automated reports that highlight treatment effectiveness and patient-specific trends. You’ll have much less time sunk into reviewing records line-by-line and more to deliver personalized care.
How to analyze EMR data
There are plenty of ways to use EMR data, but it can be challenging how to sort and analyze it in the first place. Here’s a basic step-by-step:
- Define objectives: Identify what you want to learn from the data, such as measuring patient adherence or tracking symptom improvement.
- Extract relevant data: Use EMR filters to isolate necessary data such as demographics, diagnoses, or treatment responses.
- Clean the data: Ensure the data is free of duplicates, errors, or inconsistencies.
- Analyze trends: Utilize reporting tools to identify patterns or correlations.
- Visualize results: Use graphs, charts, or dashboards to present findings in an easy-to-digest format.
- Apply insights: Integrate findings into clinical workflows and treatment decisions.
When you actually analyze your EMR data rather than just capturing it for billing purposes or using it on a patient-by-patient basis, you can make meaningful changes that improve the outcomes of the patients you treat.
Predictive analytics can be a game-changer for your practice
Predictive analytics, powered by machine learning algorithms, uses historical data to forecast future outcomes. Within EMR systems, predictive analytics can help mental health professionals anticipate patient needs before they arise. Here are some ways you can use predictive analytics:
- Crisis intervention: Identifying high-risk patients through analysis of behavioral and clinical data.
- Medication response prediction: Estimating how patients may respond to certain drugs based on population-level data.
- Care demand forecasting: Predicting future appointment needs to ensure adequate staffing levels.
Rather than just monitoring your current census, you can make accurate predictions about the future needs of your patients and community using predictive analytics tools. That way, you can become a community partner in better mental health.
Case study: Integrating Sunwave’s data-driven EMR system
Northsight Recovery partnered with Sunwave Health to leverage their EMR platform, streamlining operations and improving client outcomes. By implementing advanced tools for billing, CRM, and clinical management, the facility reduced administrative burden, enhanced workflow efficiency, and improved client engagement. This comprehensive approach allowed Northsight Recovery to focus more on delivering quality care, ultimately driving better results for their clients.
Make the most of your data with Sunwave Health
There’s more power in your data than you might realize, and we’re here to help you unleash it. With robust reporting, AI-assisted notetaking, and an EMR that seamlessly works with CRM, telehealth, and RCM tools, you’ll be able to make accurate predictions and meet the needs of your patients and community.
Schedule a demo today or call 561.576.6037 and find out how easy Sunwave makes turning day-to-day operations into year-over-year returns.
