Is there an impact of Health Information Technology on Delivery and Quality of Patient Care?

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1 Is there an impact of Health Information Technology on Delivery and Quality of Patient Care? Amanda Hessels, PhD, MPH, RN, CIC, CPHQ Nurse Scientist Meridian Health, Ann May Center for Nursing

2 2 In the next hour we will 1. Describe background of this study research hypotheses methodology and key variables 2. Review key descriptive and inferential findings 3. Discuss legislative and fiscal context implications for practice and policy

3 3 Funding and Acknowledgements "This project was supported by grant number R36HS from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. The Nurse Survey Data was funded by research grant # awarded to Dr. Flynn from the RWJF New Jersey, 2006, State Inpatient Databases (SID), Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality HIMSS Dorenfest Institute Linda Flynn, PhD, RN, FAAN; Associate Dean & Professor, College of Nursing University of Colorado, Denver (CHAIR) Suzanne Bakken, RN, PhD, FAAN, FACMI, Columbia University, School of Nursing; The Alumni Professor of Nursing and Professor of Biomedical Informatics Edna Cadmus, PhD, RN, NEA-BC, FAAN, Rutgers University, College of Nursing; Clinical Professor & Specialty Director, Nursing Leadership Program Jeannie Cimiotti, PhD, RN, FAAN, Rutgers University, College of Nursing; Executive Director, NJ Collaborating Center for Nursing and Associate Professor Robyn R. Gershon, MHS, DrPH, University of California, San Francisco; Professor Department of Epidemiology and Biostatistics, Philip R. Lee Institute for Health Policy Studies, School of Medicine

4 4 Background & Significance 13.5% of Medicare beneficiaries experience an adverse event 44% are deemed preventable Sustained high rates: 40.2/1,000 patient days $4.4 billion dollars annually

5 5 Background & Significance Sustained rates of adverse events in US hospitalized patients Dissatisfied healthcare consumers Missed Nursing Care External forces /Demand for Healthcare transformation

6 6 The Promise: Electronic Health Record (EHR) A promising system initiative aimed at: Improving clinical communication Supporting clinical decision making Providing reminders or cues regarding care activities that need to be performed Reducing adverse patient events Reducing time spent in redundant documentation

7 7 Empirical Gaps Majority of outcomes are provider reports of quality care Limited evidence that EHR is linked to improved patient outcomes No studies that link EHR to PLOS or early readmissions No studies that evaluate the impact of EHR on specific nursing care processes Relationship between missed nursing care and EHR, and evaluation of missed nursing care as an operant mechanism by which EHR relates to patient outcomes and satisfaction is untested

8 8 Hypotheses Higher levels of EHR adoption are associated with: 1. more complete delivery of nursing care 2. lower rates of patient mortality and nonmortality adverse events 3. higher levels of patient satisfaction

9 9 Design & Sample This secondary analysis of cross-sectional data from 4 sources: 1. State Inpatient Databases (SIDS) available from AHRQ (HCUP) was the data source for mortality and non-mortality adverse events 2. Healthcare Information and Management Systems Society (HIMSS) Dorenfest Institute database provided EHR adoption data 3. Centers for Medicare and Medicaid Services data from the Hospital Consumer Assessment of Healthcare Providers and Systems Survey (HCAHPS) provided measures of patient satisfaction 4. New Jersey nurse survey was the source of metrics on nursing practice environment and details of missed nursing care

10 10 Design & Sample Nurses and Patients: data was representative No gender, racial, ethnic group excluded 21 y.o. and older No recruitment of additional subjects Hospital Exclusions: psychiatric and non-acute care; < 50 admissions or < 10 nurse respondents Final analytic sample: 854,258 adult patients 70 New Jersey hospitals in ,679 nurses

11 11 Independent Variables: EHR Stages of Electronic Health Record Adoption (EMRAM) Stage Cumulative Capabilities 0 All 3 ancillaries not installed: laboratory, radiology, pharmacy 1 All 3 ancillaries installed: laboratory, radiology, pharmacy 2 Clinical data repository (CDR), controlled medical vocabulary, clinical decision support system (CDSS), health information exchange (HIE) capable, may have document imaging, and Stage1 applications 3 Nursing and clinical documentation (flow sheets), CDSS (error checking), picture archiving and communication systems (PACS) available outside of radiology, and Stage 1 and 2 applications 4 Computerized Physician Order Entry (CPOE), Clinical decision support (clinical protocols), and Stage 1, 2, and 3 applications

12 12 Independent Variables: Nursing Practice Environment Nursing Work Index (PES-NWI): 5 domain, 31-item 4-point Likert-type (strongly disagree to strongly agree) Composite and subscale scores: participation in hospital affairs foundations for quality care nurse manager ability, leadership, and support collegial nurse-physician relations staffing and resource adequacy

13 13 Independent Variables: Missed Nursing Care Which of the following activities were necessary but left undone because you lacked the time to complete them? Composite = average count of the 12 nursing care activities left undone by each nurse respondent (percentage of unmet nursing care needs per hospital) pain management oral hygiene treatments/procedures prepare patients for discharge develop/update care plans comfort/talk with patients adequate patient surveillance skin care teach patients/family administer medications on time document nursing care coordinating patient care

14 14 Dependent Variables: Patient Safety Indicators (PSIs) Calculated based on AHRQ guidelines for each measure; DRGs, ICD-9-CM codes, and appropriate risk-adjustment methodologies PSIs were calculated as rates (number of complications/ 1,000 eligible hospital discharges) Select PSIs: Death in low-mortality DRG s (PSI 2) Failure to rescue (PSI 4) Postoperative sepsis (PSI 13) Central venous catheterrelated blood stream infection (PSI 7) Postoperative hip fracture (PSI 8)

15 15 Dependent Variables Length of Stay (LOS) Continuous variable = days of patient date of discharge - date of admission Prolonged Length of Stay (PLOS) PLOS = number of hospitalization days by which a patient s stay is considered prolonged by identifying the prolongation point Readmissions 7 day all cause readmissions from HCUP SIDS Patient Satisfaction HCAHPS is a national, standardized database of a 27-item survey, reported as a set of 10 measures, of patients hospital experiences in short-term, acute care hospitals.

16 16 Control Variables 1. Nurse staffing levels ratio of patients to registered nurses in each hospital 2. Nurse education percentage of staff RNs with a baccalaureate degree or higher 3. Hospital size less than or equal to 100 beds, 101 to 250 beds, or greater than or equal to 250 beds 4. Teaching status the trainee-to-bed ratio, (number of medical residents and fellows) and categorized as minor teaching (less than 1:4 residents to trainee ratio) or major teaching (greater than 1:4 ratio) 5. High technology status facilities with open-heart surgery, major organ transplant, or both 6. Hospital geographic categories based on United States rural-urban continuity codes (Rural-Urban Continuum Codes) of the county where the hospital is located 7. Patient risk adjustment ICD9-CM primary and secondary diagnosis codes, age, sex, race, and insurance type, operationalized including a comprehensive set of 30 comorbidities

17 17 Analysis Analysis at hospital level; sample sizes sufficient to ensure robust models (probability of a Type 1 error set at.05) Tests of normality, assessed for outliers and missing data (no imputation) Risk Adjustment (AHRQ-Elixhauser risk adjustment method) Ordinary Least Squares (OLS) Multiple regression analyses Clustering: Robust procedures with Huber-White sandwich variance estimators and clustered means

18 18 Descriptive Statistics of Predictors by Hospital (N = 70) Independent Variables M SD Range Composite Nursing Practice Environment to 3.08 Staffing and resource to 2.88 Foundations for quality to 3.32 Nurse-physician relations to 3.15 Hospital affairs to 3.17 Nurse manager leadership to 3.00 Missed Nursing Care to 0.27 EHR Adoption Stage to 4 Note. Nursing practice environment measured on 1-4 scale with >2.5 indicating better work environment. Missed nursing care is average of 12 possible tasks left undone such that higher number indicates more necessary care left undone (each item missed = 0.083). EHR adoption scale 0-4 with higher number indicating more advanced adoption.

19 Descriptive Statistics of Patient Outcomes by Hospital (N = 70) Outcome Variables M SD Range Death in low-mortality DRG s (PSI 2) Failure to rescue (PSI 4) Central venous catheter-related blood stream infection (PSI 7) Postoperative hip fracture (PSI 8) Postoperative sepsis (PSI 13) Readmission within 7 days of discharge* Length of Stay (LOS) Prolonged Length of Stay (PLOS) *Readmissions N = 49. Note. PSI expressed in rates per 1,000 discharge, length of stay (LOS) measured in average days per hospital. Other outcomes expressed as cluster mean of patients per hospital with event to account for clustering of patients in hospitals. 19

20 20 Descriptive Statistics of Patient Satisfaction by Hospital (N = 41) Outcome Variables M SD Range MD communicates well RN communicates well Receive help quickly Pain well controlled Medications explained Environment clean Environment quiet Given discharge information High rating for hospital (9-10) Definitely recommend hospital to to to to to to to to to to 84 Note. Patient satisfaction responses are top box; highest rating or response of always.

21 21 What did I find? Key findings indicate: 1. EHR adoption stage is inversely related to adverse events 2. a supportive nursing environment is positively related to patient satisfaction and inversely related to missed nursing care 3. missed nursing care is inversely related to patient satisfaction

22 22 Effects of EHR Adoption Stage on Adverse Outcomes (N = 70) Unadjusted Adjusted Outcome Variable β R 2 F β R 2 F Death in low-mortality DRG s (PSI 2) Failure to rescue (PSI 4) Central venous catheterrelated blood stream infection (PSI 7) Postoperative hip fracture (PSI 8) Postoperative sepsis (PSI 13) Readmission within 7 days of discharge -0.30* Length of Stay (LOS) Prolonged Length of Stay (PLOS) * *p <.05

23 23 Effects of Composite Nursing Practice Environment on Patient Satisfaction (N = 41) Unadjusted Adjusted Outcome Variable β R 2 F β R 2 F MD communicates well 0.32* RN communicates well 0.41* Receive help quickly Pain well controlled Medications explained Environment clean Environment quiet Given discharge 0.38* information High rating for hospital 0.48* * (9-10) Definitely recommend hospital 0.54* * *p <.05

24 24 Effects of Staffing and Resources on Patient Satisfaction (N = 41) Unadjusted Adjusted Outcome Variable β R 2 F β R 2 F MD communicates well 0.41* * RN communicates well 0.52* * Receive help quickly 0.43* Pain well controlled Medications explained 0.41* * Environment clean 0.46* Environment quiet Given discharge 0.46* * information High rating for hospital (9-0.55* * ) Definitely recommend hospital 0.58* * *p <.05

25 25 Effects of Nursing Practice Environment on Missed Nursing Care (N = 70) Variable β R 2 F Composite Nursing Practice Environment -0.67* Subscales Staffing and resource -0.77* Foundations for quality -0.58* Nurse-physician relations -0.56* Hospital affairs -0.47* Nurse manager leadership -0.61* *p <.01

26 So, who cares about missed nursing care? 26

27 27 Effects of Missed Nursing Care on Patient Satisfaction (N = 41) Unadjusted Adjusted Outcome Variable β R 2 F β R 2 F MD communicates well RN communicates well Receive help quickly Pain well controlled Medications explained Environment clean Environment quiet Given discharge information High rating for hospital (9-10) Definitely recommend hospital -0.32* * *p <.05

28 28 Effects of EHR, Adjusted for Staffing and Resources and Missed Nursing Care, on Patient Satisfaction (N = 41) Unadjusted Adjusted Outcome Variable β t p β t p MD communicates well RN communicates well Receive help quickly Medications explained Environment clean Given information * High rating Definitely recommend *p <.05

29 29 Legislative Context and Financial Implications American Recovery and Reinvestment Act (ARRA) Meaningful Use: EHR incentive payments = $2 million base payment between FY (Medicare) or (Medicaid) > $5 billion dollars to date 2015: EHR payment adjustments - $ millions eligible hospitals that do not demonstrate Meaningful Use The EMRAM stages measured in this study would correspond to Meaningful Use Stages 1 and 2

30 30 Summary of Findings Higher Stages of EHR adoption were a statistically significant predictor of one adverse outcome, PLOS. 1 EHR adoption stage 1% percent decrease in patients with PLOS. Conceivably, it may be that once a tipping point of both longer duration of EHR adoption and advanced stages (EMRAM 3 or higher) is reached, the benefits of EHR will become fully evident.

31 31 Legislative Context and Financial Implications Affordable Care Act (ACA) More Satisfied patients = More $$$$$ 30% of the 1% at risk base DRG hospital operating payment in FY 2013, rises to 2% by FY 2017 Better environment = More Satisfied Patients

32 32 Summary of Findings 1 point any PES-NWI subscale % care missed 1 care task missed 4.4% definitely recommending 1 point composite PES-NWI 16% patient satisfaction 1 point Staffing & Resource % patient satisfaction

33 33 Conclusions Findings explained the relationships among: 1. EHR adoption stage and PLOS and readmissions 2. Nurse practice environment and patient satisfaction 3. Nurse practice environment and missed nursing care 4. Missed nursing care and patient satisfaction.

34 34 Key Take Home Message While EHR adoption shows signs of promise, when integrating technology into practice it is imperative that we do not overlook the fundamentals of quality nursing care: a supportive practice environment with sufficient resources to do the important work nurses do every day

35 35 Implications for Policy and Practice Good nurse practice environments, adequate staffing, and sufficient resources for the provision of nursing care are crucial in that they demonstrate a strong impact on the delivery of quality care and patient satisfaction. In context of the financial constraints, it will be necessary for organizations to redefine the delivery of healthcare in terms of value and non-value added nursing work, work-design and skill mix.

36 36 Limitations Cross-sectional (correlations not causality) Data precision Linkage of nurse processes to patient outcomes Analysis at hospital level limits sample size Voluntary data sources (selection bias- patients and procedures, EHR data submission)

37 Thank You for Your Time and Interest! 37

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