Introduction
The emergence of advanced mobile devices and applications has revolutionized the way people perceive the service sector now. As a result, a similar revolution has been witnessed in the healthcare sector where patients started expecting high-quality care in the hospitals and medical centers. Generally, the term “Care” refers to personalized care and medication, the “one for all” fundamental belief system is no more accepted across the world. Hence, the need for profound technology to make the clinical experience worthwhile is growing with every inch of innovation. This is how Clinical Analytics made its first impression in the industry.
What is Clinical Analytics?
It is a data-driven technological field where real-time medical data is collected to generate insights that further facilitate in decision –making, enhance revenue scale, and save costs. The global acceptance of Clinical Analytics has contributed immensely to reducing medication errors leading to improved population health at large. Moreover, the adoption of Clinical Analytics by several professionals and institutions across the globe has evoked a sense of competition and each one aims to thrive smartly amidst the changing government regulations and survive longer. As a result, the end-user of the service Patients are able to receive more precise and personalized care and treatment.
Clinical Analytics and Clinical Decision Support (CDS)
The Clinical Decision Support (CDS) works in close proximity with AI-driven Clinical Analytics which supports the patient’s healthcare journey by making the apparel more easily accessible to them. Furthermore, CDS tools are also responsible for fostering problem-solving ability and providing required guidance. As a result, it acts as the information and communication technology to provide relevant knowledge to ensure personalized healthcare and the wellbeing of the patient. Evidently, the users of CDS technology include allied health professionals, doctors, nurses, patients, pharmacists, technical staff, scientists, etc.
The CDS system supports two major types of information flow in the healthcare sector named Synchronous and Asynchronous which can be both active and passive in nature. Synchronous CDS is used to generate standardized information such as laboratory results, pharmacy orders, alerts, etc. On the Contrary Asynchronous CDS focuses on providing feedback once the index decision is made and implemented. The feedback so generated helps to identify opportunities and trigger reflective practices.
Use Cases of Clinical Analytics
There are several healthcare aspects where Clinical Data Analytics has reshaped the processes, procedures, and protocols As a result, its use cases have extended beyond the confined zone of medical institutions.
| Use Cases | Description |
| Biostatistician Programming | There are several healthcare aspects where Clinical Data Analytics has The drug development industry is seen focusing on research designs and data collection technologies to frame evidence-based and data-driven insights. As a result, Clinical Analytics is used to develop biostatistician programs to facilitate the discovery of the required drugs. |
| Clinical Data Management | Using Clinical Analytics service providers can easily collect and manage clinical data. The process is backed by the potent combination of the latest AI and Machine Learning technologies. As a result of which identifying and resolving data discrepancies and quality becomes a smooth task. |
| Supporting Functional Operations | The personalized and dedicated approach of Clinical Data Analytics helps to streamline projects to portfolio-related functional services in the healthcare and wellness industry. Its custom resource model allows allocating the right resources to the right projects and enhancing effective execution and control. |
| Pharmacovigilance | The advanced technological attributes of Clinical Analytics help to mitigate the adverse events in the patient’s life by focusing on the patient’s lifespan. Moreover, the early detection of the illness, diseases, or severity helps to plant the right treatment plans in the place and eliminate the growing health risks. |
| Site Management | The successful Clinical Research Trial undergoes several phases where data integrity plays an essential role. As a result, to build the transparency and accuracy of Clinical Trials Clinical Analytics facilitate Site Management. Here, the complex therapeutics areas, patient safety, stakeholder expectations, trial compliances everything is taken care of. |
Benefits of Clinical Analytics
Clinical Analytics is a refined system where the implications and benefits of CDS are leveraged in an extended manner. As a result, the overall purpose of Clinical Analytics overlaps with the goals of the Clinical Decision Support (CDS) system.
- Clinical Analytics harnesses the raw data to generate facts and support the process of diagnostic and therapeutic decision-making.
- The use of advanced AI-driven techniques reduces manual indulgence leading to increased revenue and reduced cost.
- Moreover, the existence of predictive data analytics in the Clinical ecosystem provides true forecasts and analyses of the potential opportunities and threats.
- Clinical Analytics possess the ability to enhance care coordination and deliver personalized care and medication to the patients at the right time.
- Particularly, the effective use of Clinical Analytics also lowers the costs by eliminating the overheads, labor, and supplies.
Metrics used for Clinical Analytics
Clinical Analytics is a result of Clinical Trials and operations where several factors play a crucial role. These factors can be fondly termed as “Operational Performance Metrics” where site/sponsor relationship seems to gain traction. Moreover, clinical analytics is largely concerned with the internal KPIs in the healthcare sector. As a result, we focus here on the Clinical Facility KPIs and Emergency Department and Healthcare KPIs as the key metrics of Clinical Analytics.
Clinical Facility Metrics
These metrics are focused on training and safety of the staff and patient respectively. Also, these metrics are used in conjunction with the operational and financial metrics in the HealthCare sector.
| Metrics | Description |
| Training per department | Oftentimes it has been observed that technological up-gradation and advancement are unable to give optimal desired results or responses in the organization. The biggest reason for the same is untrained or unskilled staff taking the charge. As a result of which the fullest potential of the technology is not leveraged. Hence, the institutions shall focus on analyzing how well the staff is trained to use Clinical Analytics in the daily course for the well-being of the patient. ‘ |
| Error Rate | As the name suggests, this rate measures the number of mistakes made by the staff in delivering the medical services while treating the patient. Hence, it acts as the most sensitive metric as this is what helps to define the effectiveness of the staff and Clinical Analytics (technology). Furthermore, it is calculated by dividing the number of treatment errors by the total treatments and converting it to the percentage. |
| Patient Safety | This metric takes care of the aspect of whether or not does the clinical facility keeps the patient safe. Thus, Patient Safety as a metric helps to define the effectiveness of quality care offered to the patients to keep them safe from new infections, injuries, or complications. |
| Readmission Rate | Such metric takes into account the percentage figure of the patients who are being readmitted to the hospital for the same condition /complication that was treated earlier too. It is calculated as the number of readmissions per number of discharges and converted to a percentage. The higher the readmission rate, the lower is the efficiency of the doctors and staff and vice versa. |
| Cancellation Rate | This KPI is suitable for both outpatient clinics and hospitals and deals with the missed or canceled scheduled appointments which lead to resource wastage. Alongside, this also creates room for the creation of an unhealthy relationship between the patients – physician/specialist relationship. |
Clinical Emergency Metrics
These metrics are of extreme importance as this is where the real test of efficiency takes place. Thus, Emergency Department KPIs focus on the operating upkeeps of the department while analyzing the scope for improvement.
| Metrics | Description |
| Symptoms onset to Hospitalization time | Oftentimes it is seen that patients remain clueless as to when they should start seeking medical consultation. As a result, this metric holds great importance and the sooner the patient acknowledge their symptoms, the better they are treated. Using this metric the critical stages of the patients can be easily identified and treated. Therefore, the lesser the time duration between symptoms onset and hospitalization is lesser the risk rate is. |
| Patient Mortality Rate | These healthcare metric talks about the percentage of patients passing away while in the hospital’s care. Hence, it indicates the hospital’s ability to stabilize the patient’s risky stage. However, the average industrial rate is observed at 2% yet the care facilities shall try to target it below 2%. This can be calculated using the formula = Number of Patients Deaths / Total number of patients * 100. |
| Emergency Room Wait Time | This Metric measures the time between the arrival in the emergency department and allocation of care facility to the patient. Using this metric clinics and hospitals can identify their rush hours and accordingly resource specialists in the positions. Emergency Room Wait Time is calculated Total Wait Time divided by Number of Patients. |
Clinical Care Quality Metrics
Clinical Care Metric deals with the direct impact of Clinical Facility on the Patient and their satisfaction level. Hence, such metrics also define the performance gaps of the medical staff.
| Metrics | Description |
| Patient Follow-up Rate | It indicates the number of patients who receive follow-up after their main medical treatment. This follow-up can be done by a physician, nurse, or other staff to assure improvement in the patient. Further, this metric can be used as an extended aspect of the readmission rate and is defined as a percentage figure by dividing the number of follow-ups by the total number of patients. |
| Staff – Patient Ration | As the name indicates this metric defines the quality of clinical care that a patient receives and it majorly depends on the right Staff to patient ratio. Keeping the standard or better number balance between the staff and patient is the mandatory aspect of building a healthy Clinical Care structure. |
| Patient Satisfaction | The more patient is satisfied, the better it indicates the efficiency of the care units. Lower satisfaction level of the patient is the big problem and shall be replaced with improved facilities and patient handling staffs. |
Clinical Analytics Companies and Software
Many companies have joined the Clinical Analytics space with their profound software and are giving a big boost to the Healthcare Industry. Some of the emerging companies in the domain are as follows.
| Companies/ Software | Description |
| CliniPace | The company aims to dive deeper into Healthcare analytics and bring the most refined technology to support clinical trials, drug discovery, site management, statistical programming, etc. It can be considered as the full-packed clinical data and technological solution where all aspects are sincerely considered and delivered. |
| Syntellis Performance Solutions | It is the planning, analysis, performance, and improvement platform that offers robust Enterprise Performance Management (EPM) software to healthcare institutions for supporting their clinical and patient journey. Syntellis make the best use of Data Analytics concepts in bridging the performance gaps in the healthcare sector too. |
| Elluminate | This flexible platform is the best fit for the existing clinical technological ecosystems which is accelerating data review trials and making it available for improving data quality and insight formation. The tool collects data from multiple clinical data sources and maximizes the value of the data. |
| Parexel | It is the global Clinical Research Organization (CRO) that is known to conduct Clinical Trials for pharmaceutical companies and clients. Hence, enhancing the drug discovery and development process. Moreover, the company is focused on developing innovative therapies and world-class therapies. |
| ClinHUB | It is the revolutionary data aggregation and integration platform which helps to develop a flexible ecosystem while highlighting the outliners and ongoing trends in the Clinical ecosystem. This platform is potent at loading both structured and unstructured data from multiple sources and formats to facilitate effective reporting, management, and controlling. |
| Clin ACT | This is one of the RBM solutions which offer two data software named Analytics and Integrated Risk Module which act as complementary models to each other. The former facilitates study oversight whereas the latter completes the RBM structure. |
Clinical Data Analyst
Clinical Data Analysts are the professionals responsible for developing and installing the software and training the staff as to how information shall be used correctly. Their key responsibility lies in supervising the data management staff and helping them analyze data for area-specific improvements. Also, a Clinical Analyst is bestowed with the role to optimize workflow and streamline documentation and reporting processes. Based on their analysis these professionals communicate with medical personnel the classification and practices required.
However, there are certain key skills required to work as a Clinical Analyst which includes strong leadership, time management, and critical decision-making skills. Furthermore, professionals with an understanding of HIPAA and privacy norms are preferred. Additionally, fluency with SQL and other programming languages is an added advantage.
Final Words
Clinical Analytics has reshaped the way healthcare is delivered to the patients. Moreover, its essential use case of identifying the severity level at the initial stage helps to reduce the death rate and improve recovery rate. Alongside, its fundamental and operational benefits to the medical institutions and clinical unit can not be underestimated either because it helps to balance both revenue and cost structure of the institution due to precise forecast where assumption game takes the back seat. Hence, Clinical Analytics is here to stay and revolutionize the way the healthcare industry offers its services.
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