How Mayo Clinic Harnesses Healthcare Analytics to Elevate Patient Care

Let's start with a bit of background on Mayo Clinic. It's one of the leading nonprofit healthcare providers in the world, with a prestigious legacy dating back to 1889. Over the decades, Mayo Clinic has grown into a global powerhouse, serving patients across all 50 states and from over 130 countries worldwide, employing more than 63,000 healthcare professionals.

That's an incredible journey, right? And a big part of their success story is their focus on delivering exceptional patient care through advanced healthcare analytics. By collecting and analyzing vast amounts of patient data, Mayo Clinic has been able to uncover valuable insights into clinical decision support, patient risk stratification, resource optimization, and patient experience strategies.

So, how did they achieve this? By embracing the power of healthcare analytics and using it to shape a data-driven, patient-centric approach to care delivery – one that is well-equipped to improve patient outcomes, enhance operational efficiency, and drive continuous innovation in medical treatments and therapies.

About Dataset

The hospital patient dataset provides a comprehensive overview of patient information, encompassing biographical details (patient_id, patient_gender, patient_age, patient_race), along with (patient_sat_score), identifiers (patient_first_initial, patient_last_name), administrative status (patient_admin_flag), and healthcare logistics (patient_waittime, department_referral, time_slot).

This dataset facilitates analyses of patient demographics, backgrounds, and healthcare service delivery patterns. It enables healthcare professionals to identify potential bottlenecks, streamline patient flow, and optimize resource allocation based on factors such as wait times, referral sources, and scheduling. With its diverse range of attributes, this dataset offers a holistic perspective on patient characteristics and healthcare operations, empowering data-driven decision-making and continuous improvement in patient care. Dataset consist of 12 columns and 9217 rows

Dataset Columns

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As a Data Analyst in healthcare analytics it is crucial to address extensive datasets. Our five-step plan focuses on understanding key aspects of healthcare data analysis. By utilizing healthcare analytics, patterns in patient outcomes, treatment efficacy, and resource utilization across demographics and conditions can be identified. This enables healthcare professionals to customize treatment plans, optimize resource allocation, and improve the quality of care delivery. Through analyzing patient data, healthcare organizations can identify areas for improvement, streamline processes, and make informed decisions to provide the best possible care.

Step 1: Identify the user or stakeholder for dashboard

The first step involves determining the intended recipients of the data, in this scenario, physicians and healthcare providers. Physicians and providers handle numerous responsibilities like evaluating patient medical histories, developing treatment plans, monitoring patient progress, and ensuring adherence to clinical best practices. 

These stakeholders need in-depth patient data to make well-informed decisions, such as details on medical histories, total patient visits, treatment outcomes, medication responses,patients satisfaction level  and clinical research findings. They face challenges like reducing medical errors, managing patient loads, ensuring adherence to best practices, and providing personalized care. Identifying the specific needs and pain points of these stakeholders is crucial for developing healthcare analytics that provides actionable insights and supports data-driven decision-making in improving patient outcomes and the quality of care delivered.

Step 2: Design Empathy Map

To truly connect with the experiences and expectations of a Healthcare Provider, the creation of an empathy map is invaluable. This visual tool allows for a deeper understanding of the emotions, aspirations, and pain points of users. 

By empathizing with their perspectives, we can design a data story that not only meets functional requirements but also resonates with the human elements of their roles.

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Step 3: Identify the Key Performance Indicators (KPI’s)

Once an empathy map is ready. As a data analyst, you need to decide on the most important things to keep an eye on, called KPIs (Key Performance Indicators).The heartbeat of any analysis lies in KPI’s and their Metrics. It's crucial to identify the KPIs that matter most to achieving the defined objectives and by focusing on the most relevant metrics, organizations can gain actionable insights into Healthcare Analytics.

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Step 4: Understand the Goals & Objectives of User

Based on the Empathy Map and KPIs, it is essential to establish the goals and objectives of the Users. This will help align with the data story functionalities for effective decision making. Here are the Key Objectives & Goals:

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Objective :  Analyze patient data and healthcare operations to improve patient outcomes, enhance the quality of care, and optimize resource utilization. Use data to understand patient satisfaction, visit patterns, readmission rates, and clinical performance. The aim is to identify areas for improvement and introduce strategies that boost patient satisfaction, improve care delivery, and increase operational efficiency.

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Goal : The primary goals are to increase overall patient satisfaction scores by improving communication, care coordination, and the patient experience. Analyze monthly patient visit patterns to optimize staffing and resource allocation for timely and efficient care delivery. Reduce readmission rates through enhanced discharge planning, care transitions, and post-discharge follow-up, ultimately improving patient outcomes and lowering costs. Improve clinic utilization by streamlining appointment scheduling and reducing no-show rates, ensuring efficient resource use and maximizing access to care.

Step 5 : Ask Business Questions

Beyond KPI’s, organizations must engage in business-driven inquiry. This involves asking strategic questions that directly align with overarching business objectives.

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1) What is the monthly breakdown of patient visits?

• Out of the total 9,216 patients, August had the highest number of visits with 1,024 patients, followed by May with 989 patients. January had the lowest number of patient visits, totaling 431.

SQL CODE:

SELECT
    TO_CHAR(date, 'Month') AS Month,
    COUNT(*) AS Visits
FROM
    HospitalData
GROUP BY
    TO_CHAR(date, 'Month')
ORDER BY
    Visits DESC;

2) How do patient visits compare between AM and PM time slots?

• The number of patient visits is relatively balanced between AM and PM time slots, with 4,632 visits occurring in the AM and 4,584 in the PM.

SQL CODE:

SELECT
    time_slot,
    COUNT(*) AS visit_count
FROM
    HospitalData
GROUP BY
    time_slot;

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3) How are patients distributed among various departments based on referrals?

• The majority of patients, 5,400 in total, had no referral to a specific department. General practice received 1,840 patients, orthopedics had 995, physiotherapy saw 276, cardiology treated 248, neurology attended to 193 patients, among others.

SQL CODE:

SELECT
    department_referral,
    COUNT(*) AS patient_count
FROM
    HospitalData
GROUP BY
    department_referral
ORDER BY
    patient_count DESC;

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4) What is the gender breakdown of patients?

• The gender distribution of patients is almost equal, with males representing 49% and females 51% of the total patient population.

SQL CODE:

SELECT
    patient_gender,
    COUNT(*) AS gender_count,
    ROUND((COUNT(*) * 100.0) / (SELECT COUNT(*) FROM HospitalData), 2) AS percentage
FROM
    HospitalData
GROUP BY
    patient_gender;

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5) What is the average satisfaction score by age group and gender?

• The age group of 21-30 has the highest satisfaction score, with males rating 5.52 and females rating 5.94. The lowest satisfaction scores are found within age groups 31-40, 11-20, and those above 50.

SQL CODE:

SELECT
    CASE
        WHEN patient_age BETWEEN 11 AND 20 THEN '11-20'
        WHEN patient_age BETWEEN 21 AND 30 THEN '21-30'
        WHEN patient_age BETWEEN 31 AND 40 THEN '31-40'
        WHEN patient_age > 50 THEN 'Above 50'
        ELSE 'Other'
    END AS age_group,
    patient_gender,
    AVG(patient_sat_score) AS avg_satisfaction_score
FROM
    HospitalData
GROUP BY
    age_group,
    patient_gender
ORDER BY
    avg_satisfaction_score DESC;

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6) How do patient visits vary by age group?

• The age group with the highest number of patient visits is 50 and above, totaling 3,345. The lowest number of visits belongs to the 0-10 age group, with 1,056 patients.

SQL CODE:  

SELECT
    CASE
        WHEN patient_age <= 10 THEN '0-10'
        WHEN patient_age <= 20 THEN '11-20'
        WHEN patient_age <= 30 THEN '21-30'
        WHEN patient_age <= 40 THEN '31-40'
        WHEN patient_age <= 50 THEN '41-50'
        ELSE 'Above 50'
    END AS age_group,
    COUNT(*) AS visit_count
FROM
    HospitalData
GROUP BY
    age_group
ORDER BY
    visit_count DESC;

Outcome

1. In August, there was a peak in patient visits for the year, with 1024 registered patients, indicating a possible seasonal trend or event-driven demand in healthcare services during this month. In contrast, January saw the lowest with 431 visits, suggesting varying healthcare utilization patterns throughout the year.
   
   
2. The distribution of patient visits between AM and PM is almost equal, with 4632 visits in the morning and 4584 in the afternoon, demonstrating a steady and balanced demand for healthcare services throughout the day.
   
   
3. A significant number of patients (5400) were not referred to any specific department, illustrating a significant volume of general or non-specialized healthcare needs. Among specialized referrals, General Practice and Orthopedics were the most frequented, pointing to prevalent general health and musculoskeletal issues among the patient population.
   
   
4. There is a slight female predominance in patient visits, with females making up 51% and males 49%, reflecting a marginally higher healthcare utilization by women in the observed patient population.
   
   
5. The highest satisfaction scores are seen in the 21-30 age group, particularly among females, suggesting higher satisfaction with healthcare services in younger adults. Conversely, other age groups such as 31-40, 11-20, and those over 50 reported lower satisfaction, indicating potential areas for service improvement.
   
   
6. The over 50 age group had the highest number of visits, underscoring a greater healthcare requirement in this demographic. Conversely, the 0-10 age group had the fewest visits, highlighting lower healthcare engagement or need among younger children.
   
   

Conclusion

The Healthcare Analytics dashboard revealed crucial insights into patient demographics and satisfaction. The findings indicate a need to focus on specific areas, such as optimizing appointment scheduling to better balance AM and PM time slots, and addressing the lower satisfaction scores among age groups 31-40, 11-20, and above 50. Additionally, the data highlights the importance of thorough department referrals, as many patients had none, and the need to investigate the reasons behind the gender distribution of patients. By acting on these insights through targeted patient engagement initiatives, improved appointment scheduling, and personalized care, healthcare providers can enhance patient satisfaction and overall healthcare service quality.

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