Academic health sciences centers unite tens of hospitals and clinics and provide employment and research facilities for tens of thousands of stakeholders. Such organizations usually have a long history and they have proved their position as forward-thinking enterprises that aim to improve patient care quality. In their efforts to enhance performance, healthcare networks face high hospital readmission rates that lead to high costs. 

Hospital readmissions have been linked to negative outcomes and increased financial expenses for patients, families, and hospitals throughout the United States. According to the official data, approximately 20% of all Medicare discharges resulted in a readmission during a period of 30 days. Poor care coordination and inadequate management of care transition programs are the main causes of high hospital readmission rates.

To address this issue, healthcare systems develop care coordination programs and implement custom healthcare software to leverage the data and make well-informed improvement decisions. Relevant software tools, such as clinical quality analytics, help providers reduce readmissions and avoid costs of millions of dollars. Read this post by the Belitsoft software development company to learn more about clinical quality analytics features, and benefits.

Overview of the Challenges in Achieving Lower Hospital Readmissions

In their efforts to decrease readmission rates, healthcare networks face a list of related issues that need a solution:

  • Complex multi-step process of gathering the data related to readmission rates, leading to several months of delays in data availability and an inability to make proper enhancements.
  • Lack of data to define those patients who are at highest risk for readmission and highlight the reasons for the readmission.
  • Necessity to improve the access to data and analytics tools that would help to find the areas that require attention and track the efficiency of taken measures.
  • Breakdowns in communication between care providers, between patients and care providers, and patients and at-home nurses or social workers.
  • Uncoordinated clinician accountability, particularly when a patient’s care is divided among several providers and settings.
  • Absence of standardized discharge processes to manage transitions of care.
  • Inconsistent procedures of appointing patients for hospital follow-up in different care units driven by variations in discharge practices.
  • Unstandardized materials and instructions for patient education leading to complicating discharge processes, medical disorganization, and missed opportunities to track alarming symptoms.
  • Complicated procedures hampering the patient recovery process and resulting in additional healthcare interventions.

Functionality of Clinical Quality Analytics

Accurate data with an opportunity of timely access to it leads to informed decisions and effective performance tracking. Clinical quality analytics allow users to perform the following features:

  • Assess patients on the first day of admission with the help of an 8P risk assessment tool and submit the results in the electronic medical records (EMRs). The 8Ps include problems with medications, psychological factors, principal diagnosis, physical limitations, poor health literacy, poor social support, prior hospitalizations, and palliative care needs.
  • Start the discharge planning process after a patient arrives at the hospital and passes the 8P assessment to identify the risks for readmission.
  • Examine daily updated data regarding all-cause readmission metrics, emergency department (ED) visits, and unplanned readmissions.
  • Review the information about patient demographics, provider, discharge status, etc., and understand trends, normal or special cause variations, and areas for improving care transitions and cutting readmissions.
  • Identify certain health conditions as the main sources for readmissions to develop appropriate plans for the outpatient care management services that would include transition care calls, help with obtaining necessary supplies, home visits, etc.
  • Include the data from the analytics to the organization’s performance scorecard to evaluate progress on 30-day all-cause readmissions, enhance workflows, and deliver best practices throughout the system.
  • Conduct root cause analyses for a target period to trace the reasons for readmissions and create a roadmap to address them.
  • Manage high-risk patients by customizing the care plan in the EMR and integrating evidence-based practice standards.

Clinical quality analytics provide various medical experts with an opportunity to arrange their workflows efficiently. Primary caregivers are determined to participate in education meetings and discharge arrangements. Both a patient and a caregiver understand medical information and care plans as the tech-back method is applied while instructing. Pharmacists analyze medications and exclude unnecessary prescriptions. Home health services connect with patients struggling with social isolation or restricted resources.

Additional Steps for Smooth Analytics Implementation

Relying solely on analytics tools might appear insufficient for achieving expected results. Decreasing hospital readmission rates demands developing a collaborative program that would coordinate primary, specialty, and behavioral healthcare and standardize processes and technologies. Such a program can comprise key stakeholders like physician champions, lead nurses, case managers, social workers, and patient care facilitators. Members of the team regularly, e.g., once a month, discuss their performance progress, compliance with project targets, and planned activities. Together, they enhance clinical documentation by standardizing the processes of documenting patient complexities and co-morbid conditions.

Another way to support the process of reducing hospital readmissions is to apply the BOOST program. The program stands for Better Outcomes by Optimizing Safe Transitions and facilitates the discharge transition from a medical institution to home. It includes standard tools to determine high-risk patients, instruct them about their health conditions, check interactions and possible side effects of the prescribed medicine, and arrange appointments.

The role of patient care facilitators is also important in care coordination. They receive a list of at-risk patients and those patients who were readmitted during a 30-day period after the discharge. While those patients are at hospital, they receive intensive rounding and case management. After the discharge, the list is used to prioritize follow-up phone calls.

How Do Hospitals Benefit from Analytics?

Healthcare networks that have already implemented clinical quality analytics and supported it with a holistic data-based multidisciplinary approach to enhance transitions of care, notice valuable positive changes in their performance.

  • Significant relative reduction in all-cause readmission rates during 30 days.
  • Several million saved in expense mitigation.
  • Better understanding of the care plan by patients due to the teach-back method.
  • Increased patient satisfaction as a result of improved communication between patients and nursing or physician staff, and specific patient education.
  • Planning follow-up appointments a couple of days after the discharge instead of right at the time, resulting in fewer rescheduled or overlapping meetings and timely arranged primary, specialty, or behavioral health services.
  • Cooperation with long-term care, in-home healthcare services, and skilled nursing facilities to refine outpatient transitions, cut readmission costs, and guarantee appropriate assistance in the appropriate environments. 

Addressing a Healthcare Software Development Company

Data analytics companies come to healthcare software development companies like Belitsoft to receive expert advice on developing and customizing data analytics applications and platforms. Such tools allow for analyzing current business performance indicators, identifying areas for improvement, and coordinating workflows.

Integrated data platforms enable users to conveniently collect, save, process, and examine large data sets from sources like EMRs, clinic management systems, lab systems, billing and financial systems, etc. Those platforms have the following functionality:

  • Workflow automation, i.e., cleansing, standardization, and normalization.
  • Customization of scalable data warehouses.
  • Implementation of analytical tools for building dashboards, reports, and data visualizations.
  • Configuration of the data security algorithms to comply with healthcare regulations such as HIPAA.
  • Integration of machine learning and AI into analytics.

Healthcare software development companies create specialized analytical applications like Clinical Quality Analytics under request for: 

  • Real-time data updates.
  • Embedded filters by discharge date, admission diagnosis, unit, provider, and intervention.
  • Visualization of readmission trends.
  • Integrated into EMR teaching materials, available to patients upon admission.
  • Labeled patient instructions following the stoplight concept to inform patients about alarming symptoms and signs that indicate they need to contact a provider.
  • Generation of a patient plan with steps that should be taken in case of worsening condition or new symptoms.
  • Availability of standardized electronic discharge forms with a follow-up plan.

If your company is looking for experts in data analytics, data infrastructure, data platforms, HL7 interfaces, workflow engineering, and development within the cloud (AWS, Azure, Google Cloud), hybrid, or on-premises environments, the Belitsoft software development company provides outsourced services.