Quality Control

Using Data for Improving Patient Outcomes

We discuss the profound impact of data in healthcare and how it can be used to improve patient outcomes and experiences.
March 20, 2024
6 min

Data in healthcare has slowly but surely become the foundation for improving patient outcomes and experiences. As the healthcare landscape shifts towards more data-driven care strategies, being able to actively collect and properly interpret patient data has never been more important. 

In this article, we will shed light on the profound impact of data on healthcare and discuss practical ways to use data in order to ensure better care outcomes. 

Ensuring high data quality

When we talk about data collection and management in healthcare, it is paramount to maintain high standards.

Healthcare providers rely on precise, current, and easily accessible data to make informed decisions about patient care. The quality of this data directly impacts the efficacy of diagnoses, treatment plans, and patient outcomes. 

Additionally, dealing with sensitive health information requires stringent adherence to data privacy and security protocols. A commitment to safeguarding patient data will take care of legal requirements but it will also act as a trust builder between patients and healthcare providers.

Patient experience platforms and electronic health record (EHR) systems are at the forefront of enhancing the quality of healthcare data. They can automate big chunks of data collection, analysis, and reporting which minimizes errors and enables providers to access comprehensive patient histories and make more informed care decisions. 

4 data-based ways to improve health outcomes

Healthcare providers can use collected data to identify gaps, personalize treatments, and streamline operations, leading to measurable improvements in the patient experience

Let's see how that looks in practice.

List of 4 ways in which clinics can use data for improving patient outcomes.

1. Using patient feedback to fix quality gaps and provide better care

Are you listening to your patients' concerns enough? How often do patients miss appointments because they can’t reschedule it online? Do your doctors give clear instructions that are easy to follow and understand?

All of these potential quality issues can have a detrimental impact on patient outcomes. Actively asking patients for feedback is the only way to identify and correct care quality gaps that aren’t obvious.   

Take Optical Express as an example. Soon after starting to use InisderCX to collect feedback from patients, they identified a gap in patient understanding of the eye surgery aftercare process. In response, they developed more detailed post-operative care instructions. This targeted approach improved patient satisfaction and sped up the recovery process.

There are plenty of software solutions on the market that help clinics collect and analyze feedback in near real-time. Healthcare providers that use them are able to respond promptly to patient needs, ensuring that care delivery is both responsive and effective.

2. Personalizing care based on patient activation levels

Patient activation level describes the extent to which an individual is informed, engaged, and confident in managing their health. High patient activation is linked to better health outcomes because engaged patients are more likely to partake in positive health behaviors and follow treatment plans.

Personalizing care based on the Patient Activation Measure (PAM) score allows healthcare providers to tailor interventions and support according to the patient's readiness and ability to manage their health.

The table below illustrates how a provider might approach a patient depending on their activation level.

Tailoring patient support based on their PAM level. Source: The Strategy Unit

Tailored educational programs, involving patients in decision-making processes, developing personalized health care plans, and using technology (like personalized reminders for medication adherence, appointments, or preventive care​​) can all be used to improve patient engagement and ensure better treatment outcomes.

3. Improving patient outcomes with advanced analytics

There are four main types of analytics: descriptive, diagnostic, predictive, and prescriptive. They form a continuum that ranges from understanding what has happened in the past to providing insights into future actions that can be taken to achieve desired outcomes.

Descriptive analytics examines historical data through data aggregation and mining to understand what has happened in a given period. Healthcare organizations use it for:

  • identifying trends and patterns in healthcare delivery
  • reporting on patient outcomes
  • tracking the quality of care
  • monitoring hospital readmission rates.

Diagnostic analytics relies on complex statistical analyses to understand the reasons behind past events and identify causality and correlation. It can be used to: 

  • identify the factors contributing to patient readmissions
  • understand the root causes of a surge in hospital-acquired infections,
  • determine the reasons behind variations in treatment outcomes among different patient demographics.

Predictive analytics feeds historical data into predictive machine learning algorithms to identify trends, patterns, and relationships in data and predict outcomes. It is used to:

  • forecast patient admissions
  • predict the likelihood of disease outbreaks
  • identify patients at risk of developing certain conditions or experiencing adverse events.
  • enable healthcare providers to implement preventative measures and tailor care plans to individual patient needs.

Prescriptive analytics goes beyond predicting future events to suggest actions that can positively influence outcomes. It often incorporates AI and machine learning to simulate the effects of different decisions and actions. Common uses cases include:

  • recommending personalized treatment plans based on a patient's unique health profile
  • suggesting adjustments to staffing levels to meet predicted patient flow 
  • optimizing supply chain management for pharmaceuticals.

The integration of these analytics types into healthcare decision-making enables a data-driven approach to improving patient outcomes, operational efficiency, and quality of care.

4. Improving access to care by digitizing healthcare operations

It is futile to discuss outcomes if a patient didn’t have access to care in the first place.

Luckily, digital transformation is helping healthcare improve care accessibility by opening new ways to interact with patients, improving access to information, and simplifying the communication between patients and their care providers.

All of that is happening through:

  • Digital platforms that allow patients to (re)schedule appointments quickly and easily.
  • Telehealth services, which have enabled patients to consult their healthcare providers from the comfort of their homes — particularly beneficial for those living in remote areas, individuals with mobility issues, or patients seeking immediate medical advice.
  • Patient portals that offer patients 24/7 access to their health information, including test results, medication lists, and medical histories.
  • Communications apps like WhatsApp and Viber that enable healthcare providers to communicate with patients directly — sending timely appointment reminders, following-up on the recovery process, and asking for patient feedback.

The challenges in measuring outcomes and attributing improvements to specific interventions

As healthcare continues to evolve into a more data-driven industry, measuring patient outcomes — and directly linking these to specific interventions — comes with some challenges. 

Understanding and overcoming them is critical for clinicians, researchers, and healthcare administrators who aim to leverage data effectively to enhance patient care and outcomes. 

1. Data quality and consistency

  • Variability in data collection: Data collected from different sources, such as electronic health records (EHRs), wearable devices, and patient surveys, often vary in quality and format. This inconsistency can lead to challenges in integrating and analyzing data effectively.
  • Accuracy and completeness: Inaccuracies in data entry, missing information, and outdated records can skew analysis results, leading to potentially misleading conclusions about patient outcomes.

2. Complex causality in healthcare

  • Multifactorial influences: Patient outcomes are influenced by a wide range of factors, including genetics, environment, lifestyle, and socio-economic status. Isolating the impact of data-driven interventions from these variables is challenging.
  • Longitudinal impact: The effects of some interventions may take a long time to manifest. Measuring outcomes over an appropriate timescale to capture these effects can be difficult.

3. Ethical and privacy concerns

  • Patient consent and anonymity: Ensuring patient data is used ethically and maintaining privacy while conducting large-scale data analysis poses its own set of challenges. Concerns over data breaches and misuse can impact the willingness of patients to participate or share their data.
  • Bias and equity: Data sets can contain biases that, if unaddressed, lead to interventions that do not improve or even worsen outcomes for certain groups. Ensuring equity in data-driven healthcare requires constant vigilance and adjustment of models.

4. Technological and methodological limitations

  • Interoperability issues: The ability of different IT systems and software applications to communicate, exchange data, and use the information that has been exchanged is still limited in many healthcare settings, hindering comprehensive data analysis.
  • Advanced analytical skills: The shortage of professionals with the skills needed to perform complex data analysis and interpret the results can limit the ability of healthcare organizations to implement data-driven interventions effectively.

With the right software support and a culture of data literacy, clinics should have the tools to correlate actions with outcomes. At the end of the day, each healthcare provider should be concerned with improving their internal operations and care quality. If they can do that, the improvements in patient outcomes will follow suit.     

Improving patient outcomes with InsiderCX

InsiderCX is a patient experience platform that helps private healthcare providers automate quality control and provide better care.

With InsiderCX, you gain the ability to: 

  • collect patient feedback by creating and sending white labeled mobile surveys   
  • track and measure NPS and other patient satisfaction metrics
  • look at the reports to identify and eliminate quality issues
  • benchmark quality and satisfaction across locations, departments, service types , and doctors 
  • send appointment reminders and follow-ups.

All of the data we collect can be automatically forwarded to existing healthcare software you might be using.

There aren’t many other ways where you can just log in, look at the dashboard, and get so many useful data points which you can use to improve efficiency and patient outcomes.

If you want to take InsiderCX for a test run, reach out to our team.

InsiderCX Editorial Team
This article was researched, written, polished, and published by the InsiderCX editorial team.

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