Rural Healthcare Intelligence: Business and Clinical Perspective

This business case dives into the business and clinical perspective of rural healthcare intelligence.

Business intelligence (BI) is a term that elicits different meanings to different healthcare professionals (HCPs). In addition, HCPs often cannot agree on how best to define BI a, and thus, it becomes more difficult for health service organizations (HSOs), rural or urban, to establish effective BI programs of their own.

In her book “Healthcare Business Intelligence: A Guide to Empowering Successful Data Reporting and Analytics,” Laura Madsen defines healthcare BI as "the integration of data from clinical systems, financial systems, and other disparate data sources into a data warehouse that requires a set of validated data to address the concepts of clinical quality, effectiveness of care, and value for business usage.”  Thus, this definition is an ideal starting point for understanding BI in either urban or rural HSOs.

Rural Healthcare Definition

In the United States, rural populations are defined as a population residing within a county or area not designated by the Office of Management and Budget as a Metropolitan Statistical Area (MSA); at least one city with 50,000 or more inhabitants; and total population of at least 100,000.

Broadly speaking, however, rural healthcare can be defined as the provision of health services to areas outside of metropolitan centers where there is not ready access to specialist, intensive and/or high technology care, and where resources, both human and material, are lacking. This service may be within hospitals, health centers, clinics or independent practices. It is best provided by a team of healthcare workers and is based on the principles of primary healthcare.

In the United States, 75% of its land mass is considered rural with 20% of the total population living in it. That statistic translates into 1 in 20 US citizens that suffer from poor healthcare and healthcare delivery challenges due to geographical isolation, socioeconomic challenges, risky health behaviors and limited income generation opportunities. 

As a result, rural health tends to be characterized by the following components:




Figure 1: Rural Health Statistics
Rural Health: Access to Care and Services***


Delivering quality healthcare in rural settings has its unique challenges, including 

  • difficulty of hiring doctors and other healthcare professional (HCPs) to serve those populations;
  • high cost of technology for facilities with a lower income patient population;  and 
  • a sparsely insured population with limited community supported resources.

The Center for Medicare and Medicaid Services has designated 1,300 hospitals in the US Critical Access Hospitals (CAHs). This designation was meant to define small, short- stay, rural, and isolated hospitals that would benefit from cost-based reimbursement, and thereby, enhance their survival in those rural settings (Bauer, 2010). Legislation enacted as part of the Balanced Budget Act (BBA) of 1997 authorized states to establish a State Flex Program under which certain facilities participating in Medicare can become CAHs. For purposes of this document, CAHs and Rural Health Service Organizations (RHSOs) can be considered interchangeable since they both fall under the rural health umbrella.

One of the primary initiatives of the National Rural Health Association (NRHA) has been focused on identifying and sharing information on rural health projects and initiatives that have delivered positive results to improve care in certain rural populations. A program called Models that Work has outlined certain criteria that make up the foundational intelligence for inclusion into the program. The three basic criteria for rural intelligence are:




Figure 2: Three Basic Criteria for Rural Intelligence


These basic pillars of rural health intelligence appear in models that work, such as the Native American Health delivery system with its Alaskan Tribal Health and Hope Regional Health. Other models have been found in Maine and Texas.  These models have helped HCPs develop an improved understanding of the wide ranging rural health disparities throughout the United States.

The Rural Healthy People 2010 is another initiative; one housed at the University of Texas A&M and focused on intelligence in top rural health concerns. Intelligence is gathered and disseminated by this initiative on the successful practice models and literature review related to rural health disparities and models of practice. An online data base has been created and maintained by Rural Healthy People 2010 that enables HCPs to hone in on practice models in their respective states and regions with a focus on rural priority subject, such as access, diagnosis, behaviors or other designations of priority.

Under the current healthcare reform legislation, CAHs are not required to report quality measures to Hospital Compare. Public reporting or quality measures otherwise referred to as intelligence tends to motivate improvement in the quality of care and help patients make appropriate decisions in selecting healthcare providers.  Since an initial set of intelligence measures relevant to rural healthcare settings were identified in 2004, many new measures have been developed, and existing measures have been eliminated or revised.  The growing use of intelligence for reporting and payment purposes has made it critical to review the rural importance of hospital quality measures. The measures include CMS inpatient and outpatient quality reporting, Joint Commission (JNC) and National Quality Forum (NQF).

Five key elements can be considered the primary drivers of healthcare business intelligence. They include:

  • data quality,
  • leadership and sponsorship,
  • technology and architecture,
  • cultural change, and
  • value.

Data Quality: Credible and reliable data for analytics and quality improvement start with proper management of data. The deluge of data from the active use of EHRs and meaningful use demands has resulted in an exciting opportunity for use of data in healthcare quality and performance improvement activities.  Making sure that data is available and usable is an ongoing challenge.  Quality data engenders trust in and high user adoption of BI in HSOs.

Sponsorship and Leadership:  Effective leverage of BI in healthcare may appear to be a foregone conclusion. No argument against BI use could credibly be advanced by healthcare leaders.  However, in spite of that, HSOs demonstrate significant variation in the implementation of BI within their organizations. BI and analytics integration into the day-to-day functions of a HSO tends to be directly proportional to the financial, technological, and human resources committed to improving outcomes in any HSO. Especially critical to BI engagement is the kind of organizational sponsorship that delivers a knowledgeable team and long-term engagement in the process. 

Architecture and Technology:  While BI is definitely not an IT activity, it does demand the relevant framework that includes effective support activities and relevant technical infrastructure. Many HSOs now suffer from data that is siloed and maintained in a discontinuous state. To overcome the challenges associated with data in this state, HSOs will need to invest in best practices related to data modeling, extraction, transformation, and loading. BI applications must be selected by HSOs to ensure performance and scalability.  Without the proper technical and management structures, BI and analytics will be unable to provide the critical and reliable insights that are necessary for HSOs to excel in their healthcare delivery.

Value: Given that ultimately improved clinical outcomes are the goal of any BI program, HSOs must clearly take into consideration the value provided to patients. Vital questions must be, “What value do our patients receive during the course of care? Is that value measured consistently as an essential indicator of quality and performance? “ A significant way of attaining value from BI is to focus its capabilities on pain points in HSOs whose resolution would deliver high-impact and high-value change.

Culture: The main barriers to transformation of delivery of healthcare through BI are not clinical, process, or technology-related, but rather, the concern over change that comes out of fear, uncertainty, doubt and politics. BI professionals may be forced to play the role of change agents-- in healthcare; they need to harness the skills necessary to effectively collaborate with other HCPs who may not have an immediate understanding of the benefits of BI to their profession and patients. Ultimately, collaboration acts as the adhesive that drives a strong BI team. It starts with a mutual understanding of what the definition of BI is in healthcare and how it can be leveraged effectively.

Embracing the five pillars of BI outlined above provides the “how to” for HCPs  in rural environments to work together to leverage the best available healthcare evidence  to strategically execute  improvement efforts and capabilities with the goal of delivering superior clinical outcomes and business  goals. A firmly grounded knowledge and familiarity with the local environment is a fundamental piece of rural health business intelligence.  

Business Intelligence must, however, be coupled with critical healthcare intelligence that is relevant to the respective rural environment. Current measures with their corresponding exclusions may now suffice. However, we must begin to look at the demands or rural health and focus on relevant, future intelligence measures for reporting by CAHs or rural healthcare facilities.  The absence of relevant intelligence can no longer suffice for CAHs /rural hospitals failing to meet certain quality outcomes. By encouraging adoption of future rural intelligence measures and public reporting  of the same, CAHs /rural healthcare service organizations (HSOs)  will be guided  to  safer, more effective, patient-centered, ,and efficient care.

The information below represents current areas of clinical intelligence based on healthcare reform vs the future demand of clinical intelligence relative to rural healthcare.

Relevant Intelligence Measures Ready for reporting by CAHs

Pneumonia Patient

Blood culture in emergency department prior to initial antibiotic

Appropriate initial antibiotics

Heart Failure Inpatient

Evaluation of Left Ventricular Systolic Dysfunction (LVSD).

ACE/ARB for LVSD at discharge

AMI Inpatient and Emergency Department

Aspirin at arrival

Fibrinolytic therapy within 30 minutes


Aspirin at discharge

Angiotensin-converting-enzyme (ACE)  or Angiotensin Receptor Blocker (ARB) for Left Ventricular Systolic Dysfunction (LVSD)

Beta blocker at discharge

Statin prescribed at discharge

Acute Myocardial Infarction (AMI) /Chest Pain Emergency Department

Median time to fibrinolysis for AMI patients.

Median time to transfer to another facility for acute coronary intervention

Median time to electrocardiogram  (ECG)


Communication with registered nurses (RNs)

Communication with medical doctors (MDs)

Hospital staff responsiveness

Cleanliness /quietness of hospital

Pain management

Communication about medication

Discharge information

Hospital overall rating

Recommendation of hospital

Global Vaccination

Influenza vaccination overall rate

Pneumococcal immunization overall rate

Emergency Department

Door-to-diagnostic evaluation by a qualified medical professional

Median time to pain management for long bone fracture.

Head CT or MRI scan results for stroke within 45 minutes.

Median time form Emergency Department arrival to departure for admitted patients

Admit decision time to Emergency Dept. departure time for admitted patients.

Surgical Care Improvement - CAHs Providing Surgery Outpatient and Inpatient Surgery

Prophylactic antibiotic 1 hour prior to incision

Prophylactic antibiotic selection for surgical patients

Inpatient  Surgery

Prophylactic antibiotics discontinued after 24 hours

Surgery patients on beta blockers who received beta blocker during perioperative period.

Surgery patients who received appropriate VTE prophylaxis within 24 hours prior to surgical incision time to 24 hours after surgery end time.

Rationale: 2 to 24 hours after surgery

Preoperative urinary catheter removal post-op day 1-2

Preoperative temperature management

CMS Inpatient Quality Reporting /Hospital Compare measure; specifications at

CMS Inpatient Quality Reporting /Hospital Compare measure; specifications at

NQF-endorsed measure; information on all endorsed measures at

CMS EHR Meaningful Use Measure


Relevant Future Intelligence Measures for Reporting by CAHs

Stroke Patient

Discharged on antithrombotic therapy

Anticoagulation therapy for atrial fibrillation/flutter

Antithrombotic therapy by end of day 2.

Discharged on statin medication.

Stroke education

Assessed for rehabilitation.

Venous Thromboembolism (VTE)Patient

VTE prophylaxis

VTE patients with anticoagulation therapy

Incidence of potentially preventable VTE.

Global Tobacco Use

Tobacco use screening.

Tobacco use treatment provided or offered during inpatient admission or at discharge.

Emergency Department

Emergency department transfer communication measures

Care Transitions

Transition record with specified elements received by discharged Emergency Department patients.

Transition record with specified elements received by inpatient discharges.

Timely transmission of transition record for discharged inpatients to provider for follow-up care.

Care Transition Measures

Healthcare-associated Infection

Catheter-associated urinary tract infection.

Central line associated blood stream infection (CLABSI)

Surgical site infection.

Healthcare provider influenza vaccination

Methicillin-resistant Staphylococcus Aureus (MRSA) Infection rate.

Clostridium difficile infection rate

Perinatal Measures - CAHs Providing Obstetrics

Elective delivery prior to 39 weeks gestation.

C-section rate for low-risk, first birth women’s

Exclusive breastfeeding during birth hospitalization

Source: “Rural Relevant Quality Measures for Critical Access Hospitals


W John Gachago, e-Health Consultant, JWG Global Ltd., HIMSS HIT for Rural Health and Underserved Work Group Member


Special thanks to the HIMSS HIT for Rural Health and Underserved Work Group Volunteers

  • Chairperson – Gora Datta, Chairman & CEO CAL2CAL Corporation, HL7 International Ambassador &Co-chair, HL7 Mobile Health, Vice-Chair, IEEE Orange County Section
  • John Ritter, Healthcare Standards Architect; Volunteer at HL7, ISO TC215, HIMSS, ONC S&I Framework.
  • Kalyani Yerra, MBA, MHA, PMP, CPHIMS, Sr. Technical Architect, Premier, Inc. 
  • Roger Shindell, MS, CHPS, Founder, President, CEO, Carosh Compliance Solutions
  • W John Gachago, e-Health Consultant, JWG Global Ltd.
  • Larry Rine, CEO, Intersect Healthcare Systems

HIMSS Staff:

Ian E. Hoffberg, Manager, Healthcare Information Systems (HIS), HIMSS North America

If you have comments, please contact Ian E. Hoffberg at

Rural, underserved, business intelligence, healthcare